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NVIDIA-Omniverse/kit-extension-sample-asset-search/exts/omni.example.asset_provider/omni/assetprovider/template/constants.py

SETTING_ROOT = "/exts/omni.assetprovider.template/" SETTING_STORE_ENABLE = SETTING_ROOT + "enable"

Python

NVIDIA-Omniverse/kit-extension-sample-asset-search/exts/omni.example.asset_provider/omni/assetprovider/template/extension.py

# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. import importlib import carb import carb.settings import carb.tokens import omni.ext from omni.services.browser.asset import get_instance as get_asset_services from .model import TemplateAssetProvider from .constants import SETTING_STORE_ENABLE class TemplateAssetProviderExtension(omni.ext.IExt): """ Template Asset Provider extension. """ def on_startup(self, ext_id): self._asset_provider = TemplateAssetProvider() self._asset_service = get_asset_services() self._asset_service.register_store(self._asset_provider) carb.settings.get_settings().set(SETTING_STORE_ENABLE, True) def on_shutdown(self): self._asset_service.unregister_store(self._asset_provider) carb.settings.get_settings().set(SETTING_STORE_ENABLE, False) self._asset_provider = None self._asset_service = None

Python

NVIDIA-Omniverse/kit-extension-sample-asset-search/exts/omni.example.asset_provider/omni/assetprovider/template/model.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. from typing import Dict, List, Optional, Union, Tuple import aiohttp from omni.services.browser.asset import BaseAssetStore, AssetModel, SearchCriteria, ProviderModel from .constants import SETTING_STORE_ENABLE from pathlib import Path CURRENT_PATH = Path(__file__).parent DATA_PATH = CURRENT_PATH.parent.parent.parent.parent.joinpath("data") # The name of your company PROVIDER_ID = "PROVIDER_NAME" # The URL location of your API STORE_URL = "https://www.your_store_url.com" class TemplateAssetProvider(BaseAssetStore): """ Asset provider implementation. """ def __init__(self, ov_app="Kit", ov_version="na") -> None: super().__init__(PROVIDER_ID) self._ov_app = ov_app self._ov_version = ov_version async def _search(self, search_criteria: SearchCriteria) -> Tuple[List[AssetModel], bool]: """ Searches the asset store. This function needs to be implemented as part of an implementation of the BaseAssetStore. This function is called by the public `search` function that will wrap this function in a timeout. """ params = {} # Setting for filter search criteria if search_criteria.filter.categories: # No category search, also use keywords instead categories = search_criteria.filter.categories for category in categories: if category.startswith("/"): category = category[1:] category_keywords = category.split("/") params["filter[categories]"] = ",".join(category_keywords).lower() # Setting for keywords search criteria if search_criteria.keywords: params["keywords"] = ",".join(search_criteria.keywords) # Setting for page number search criteria if search_criteria.page.number: params["page"] = search_criteria.page.number # Setting for max number of items per page if search_criteria.page.size: params["page_size"] = search_criteria.page.size items = [] # TODO: Uncomment once valid Store URL has been provided # async with aiohttp.ClientSession() as session: # async with session.get(f"{STORE_URL}", params=params) as resp: # result = await resp.read() # result = await resp.json() # items = result assets: List[AssetModel] = [] # Create AssetModel based off of JSON data for item in items: assets.append( AssetModel( identifier="", name="", published_at="", categories=[], tags=[], vendor=PROVIDER_ID, product_url="", download_url="", price=0.0, thumbnail="", ) ) # Are there more assets that we can load? more = True if search_criteria.page.size and len(assets) < search_criteria.page.size: more = False return (assets, more) def provider(self) -> ProviderModel: """Return provider info""" return ProviderModel( name=PROVIDER_ID, icon=f"{DATA_PATH}/logo_placeholder.png", enable_setting=SETTING_STORE_ENABLE )

Python

NVIDIA-Omniverse/urdf-importer-extension/source/extensions/omni.importer.urdf/python/scripts/commands.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import omni.client import omni.kit.commands # import omni.kit.utils from omni.client._omniclient import Result from omni.importer.urdf import _urdf from pxr import Usd class URDFCreateImportConfig(omni.kit.commands.Command): """ Returns an ImportConfig object that can be used while parsing and importing. Should be used with `URDFParseFile` and `URDFParseAndImportFile` commands Returns: :obj:`omni.importer.urdf._urdf.ImportConfig`: Parsed URDF stored in an internal structure. """ def __init__(self) -> None: pass def do(self) -> _urdf.ImportConfig: return _urdf.ImportConfig() def undo(self) -> None: pass class URDFParseFile(omni.kit.commands.Command): """ This command parses a given urdf and returns a UrdfRobot object Args: arg0 (:obj:`str`): The absolute path to where the urdf file is arg1 (:obj:`omni.importer.urdf._urdf.ImportConfig`): Import Configuration Returns: :obj:`omni.importer.urdf._urdf.UrdfRobot`: Parsed URDF stored in an internal structure. """ def __init__(self, urdf_path: str = "", import_config: _urdf.ImportConfig = _urdf.ImportConfig()) -> None: self._root_path, self._filename = os.path.split(os.path.abspath(urdf_path)) self._import_config = import_config self._urdf_interface = _urdf.acquire_urdf_interface() pass def do(self) -> _urdf.UrdfRobot: return self._urdf_interface.parse_urdf(self._root_path, self._filename, self._import_config) def undo(self) -> None: pass class URDFParseAndImportFile(omni.kit.commands.Command): """ This command parses and imports a given urdf and returns a UrdfRobot object Args: arg0 (:obj:`str`): The absolute path to where the urdf file is arg1 (:obj:`omni.importer.urdf._urdf.ImportConfig`): Import Configuration arg2 (:obj:`str`): destination path for robot usd. Default is "" which will load the robot in-memory on the open stage. Returns: :obj:`str`: Path to the robot on the USD stage. """ def __init__(self, urdf_path: str = "", import_config=_urdf.ImportConfig(), dest_path: str = "") -> None: self.dest_path = dest_path self._urdf_path = urdf_path self._root_path, self._filename = os.path.split(os.path.abspath(urdf_path)) self._import_config = import_config self._urdf_interface = _urdf.acquire_urdf_interface() pass def do(self) -> str: status, imported_robot = omni.kit.commands.execute( "URDFParseFile", urdf_path=self._urdf_path, import_config=self._import_config ) if self.dest_path: self.dest_path = self.dest_path.replace( "\\", "/" ) # Omni client works with both slashes cross platform, making it standard to make it easier later on result = omni.client.read_file(self.dest_path) if result[0] != Result.OK: stage = Usd.Stage.CreateNew(self.dest_path) stage.Save() return self._urdf_interface.import_robot( self._root_path, self._filename, imported_robot, self._import_config, self.dest_path ) def undo(self) -> None: pass omni.kit.commands.register_all_commands_in_module(__name__)

Python

NVIDIA-Omniverse/urdf-importer-extension/source/extensions/omni.importer.urdf/python/scripts/extension.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import gc import os import weakref import carb import omni.client import omni.ext import omni.ui as ui from omni.importer.urdf import _urdf from omni.importer.urdf.scripts.ui import ( btn_builder, cb_builder, dropdown_builder, float_builder, get_style, make_menu_item_description, setup_ui_headers, str_builder, ) from omni.kit.menu.utils import MenuItemDescription, add_menu_items, remove_menu_items from pxr import Sdf, Usd, UsdGeom, UsdPhysics # from .menu import make_menu_item_description # from .ui_utils import ( # btn_builder, # cb_builder, # dropdown_builder, # float_builder, # get_style, # setup_ui_headers, # str_builder, # ) EXTENSION_NAME = "URDF Importer" def is_urdf_file(path: str): _, ext = os.path.splitext(path.lower()) return ext in [".urdf", ".URDF"] def on_filter_item(item) -> bool: if not item or item.is_folder: return not (item.name == "Omniverse" or item.path.startswith("omniverse:")) return is_urdf_file(item.path) def on_filter_folder(item) -> bool: if item and item.is_folder: return True else: return False class Extension(omni.ext.IExt): def on_startup(self, ext_id): self._ext_id = ext_id self._urdf_interface = _urdf.acquire_urdf_interface() self._usd_context = omni.usd.get_context() self._window = omni.ui.Window( EXTENSION_NAME, width=400, height=500, visible=False, dockPreference=ui.DockPreference.LEFT_BOTTOM ) self._window.set_visibility_changed_fn(self._on_window) menu_items = [ make_menu_item_description(ext_id, EXTENSION_NAME, lambda a=weakref.proxy(self): a._menu_callback()) ] self._menu_items = [MenuItemDescription(name="Workflows", sub_menu=menu_items)] add_menu_items(self._menu_items, "Isaac Utils") self._file_picker = None self._models = {} result, self._config = omni.kit.commands.execute("URDFCreateImportConfig") self._filepicker = None self._last_folder = None self._content_browser = None self._extension_path = omni.kit.app.get_app().get_extension_manager().get_extension_path(ext_id) self._imported_robot = None # Set defaults self._config.set_merge_fixed_joints(False) self._config.set_replace_cylinders_with_capsules(False) self._config.set_convex_decomp(False) self._config.set_fix_base(True) self._config.set_import_inertia_tensor(False) self._config.set_distance_scale(1.0) self._config.set_density(0.0) self._config.set_default_drive_type(1) self._config.set_default_drive_strength(1e7) self._config.set_default_position_drive_damping(1e5) self._config.set_self_collision(False) self._config.set_up_vector(0, 0, 1) self._config.set_make_default_prim(True) self._config.set_create_physics_scene(True) self._config.set_collision_from_visuals(False) def build_ui(self): with self._window.frame: with ui.VStack(spacing=5, height=0): self._build_info_ui() self._build_options_ui() self._build_import_ui() stage = self._usd_context.get_stage() if stage: if UsdGeom.GetStageUpAxis(stage) == UsdGeom.Tokens.y: self._config.set_up_vector(0, 1, 0) if UsdGeom.GetStageUpAxis(stage) == UsdGeom.Tokens.z: self._config.set_up_vector(0, 0, 1) units_per_meter = 1.0 / UsdGeom.GetStageMetersPerUnit(stage) self._models["scale"].set_value(units_per_meter) async def dock_window(): await omni.kit.app.get_app().next_update_async() def dock(space, name, location, pos=0.5): window = omni.ui.Workspace.get_window(name) if window and space: window.dock_in(space, location, pos) return window tgt = ui.Workspace.get_window("Viewport") dock(tgt, EXTENSION_NAME, omni.ui.DockPosition.LEFT, 0.33) await omni.kit.app.get_app().next_update_async() self._task = asyncio.ensure_future(dock_window()) def _build_info_ui(self): title = EXTENSION_NAME doc_link = "https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/ext_omni_isaac_urdf.html" overview = "This utility is used to import URDF representations of robots into Isaac Sim. " overview += "URDF is an XML format for representing a robot model in ROS." overview += "\n\nPress the 'Open in IDE' button to view the source code." setup_ui_headers(self._ext_id, __file__, title, doc_link, overview) def _build_options_ui(self): frame = ui.CollapsableFrame( title="Import Options", height=0, collapsed=False, style=get_style(), style_type_name_override="CollapsableFrame", horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, vertical_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, ) with frame: with ui.VStack(style=get_style(), spacing=5, height=0): cb_builder( label="Merge Fixed Joints", tooltip="Consolidate links that are connected by fixed joints.", on_clicked_fn=lambda m, config=self._config: config.set_merge_fixed_joints(m), ) cb_builder( label="Replace Cylinders with Capsules", tooltip="Replace Cylinder collision bodies by capsules.", on_clicked_fn=lambda m, config=self._config: config.set_replace_cylinders_with_capsules(m), ) cb_builder( "Fix Base Link", tooltip="Fix the robot base robot to where it's imported in world coordinates.", default_val=True, on_clicked_fn=lambda m, config=self._config: config.set_fix_base(m), ) cb_builder( "Import Inertia Tensor", tooltip="Load inertia tensor directly from the URDF.", on_clicked_fn=lambda m, config=self._config: config.set_import_inertia_tensor(m), ) self._models["scale"] = float_builder( "Stage Units Per Meter", default_val=1.0, tooltip="Sets the scaling factor to match the units used in the URDF. Default Stage units are (cm).", ) self._models["scale"].add_value_changed_fn( lambda m, config=self._config: config.set_distance_scale(m.get_value_as_float()) ) self._models["density"] = float_builder( "Link Density", default_val=0.0, tooltip="Density value to compute mass based on link volume. Use 0.0 to automatically compute density.", ) self._models["density"].add_value_changed_fn( lambda m, config=self._config: config.set_density(m.get_value_as_float()) ) dropdown_builder( "Joint Drive Type", items=["None", "Position", "Velocity"], default_val=1, on_clicked_fn=lambda i, config=self._config: config.set_default_drive_type( 0 if i == "None" else (1 if i == "Position" else 2) ), tooltip="Default Joint drive type.", ) self._models["drive_strength"] = float_builder( "Joint Drive Strength", default_val=1e4, tooltip="Joint stiffness for position drive, or damping for velocity driven joints. Set to -1 to prevent this parameter from getting used.", ) self._models["drive_strength"].add_value_changed_fn( lambda m, config=self._config: config.set_default_drive_strength(m.get_value_as_float()) ) self._models["position_drive_damping"] = float_builder( "Joint Position Damping", default_val=1e3, tooltip="Default damping value when drive type is set to Position. Set to -1 to prevent this parameter from getting used.", ) self._models["position_drive_damping"].add_value_changed_fn( lambda m, config=self._config: config.set_default_position_drive_damping(m.get_value_as_float()) ) self._models["clean_stage"] = cb_builder( label="Clear Stage", tooltip="Clear the Stage prior to loading the URDF." ) dropdown_builder( "Normals Subdivision", items=["catmullClark", "loop", "bilinear", "none"], default_val=2, on_clicked_fn=lambda i, dict={ "catmullClark": 0, "loop": 1, "bilinear": 2, "none": 3, }, config=self._config: config.set_subdivision_scheme(dict[i]), tooltip="Mesh surface normal subdivision scheme. Use `none` to avoid overriding authored values.", ) cb_builder( "Convex Decomposition", tooltip="Decompose non-convex meshes into convex collision shapes. If false, convex hull will be used.", on_clicked_fn=lambda m, config=self._config: config.set_convex_decomp(m), ) cb_builder( "Self Collision", tooltip="Enables self collision between adjacent links.", on_clicked_fn=lambda m, config=self._config: config.set_self_collision(m), ) cb_builder( "Collision From Visuals", tooltip="Creates collision geometry from visual geometry.", on_clicked_fn=lambda m, config=self._config: config.set_collision_from_visuals(m), ) cb_builder( "Create Physics Scene", tooltip="Creates a default physics scene on the stage on import.", default_val=True, on_clicked_fn=lambda m, config=self._config: config.set_create_physics_scene(m), ) cb_builder( "Create Instanceable Asset", tooltip="If true, creates an instanceable version of the asset. Meshes will be saved in a separate USD file", default_val=False, on_clicked_fn=lambda m, config=self._config: config.set_make_instanceable(m), ) self._models["instanceable_usd_path"] = str_builder( "Instanceable USD Path", tooltip="USD file to store instanceable meshes in", default_val="./instanceable_meshes.usd", use_folder_picker=True, folder_dialog_title="Select Output File", folder_button_title="Select File", ) self._models["instanceable_usd_path"].add_value_changed_fn( lambda m, config=self._config: config.set_instanceable_usd_path(m.get_value_as_string()) ) def _build_import_ui(self): frame = ui.CollapsableFrame( title="Import", height=0, collapsed=False, style=get_style(), style_type_name_override="CollapsableFrame", horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, vertical_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, ) with frame: with ui.VStack(style=get_style(), spacing=5, height=0): def check_file_type(model=None): path = model.get_value_as_string() if is_urdf_file(path) and "omniverse:" not in path.lower(): self._models["import_btn"].enabled = True else: carb.log_warn(f"Invalid path to URDF: {path}") kwargs = { "label": "Input File", "default_val": "", "tooltip": "Click the Folder Icon to Set Filepath", "use_folder_picker": True, "item_filter_fn": on_filter_item, "bookmark_label": "Built In URDF Files", "bookmark_path": f"{self._extension_path}/data/urdf", "folder_dialog_title": "Select URDF File", "folder_button_title": "Select URDF", } self._models["input_file"] = str_builder(**kwargs) self._models["input_file"].add_value_changed_fn(check_file_type) kwargs = { "label": "Output Directory", "type": "stringfield", "default_val": self.get_dest_folder(), "tooltip": "Click the Folder Icon to Set Filepath", "use_folder_picker": True, } self.dest_model = str_builder(**kwargs) # btn_builder("Import URDF", text="Select and Import", on_clicked_fn=self._parse_urdf) self._models["import_btn"] = btn_builder("Import", text="Import", on_clicked_fn=self._load_robot) self._models["import_btn"].enabled = False def get_dest_folder(self): stage = omni.usd.get_context().get_stage() if stage: path = stage.GetRootLayer().identifier if not path.startswith("anon"): basepath = path[: path.rfind("/")] if path.rfind("/") < 0: basepath = path[: path.rfind("\\")] return basepath return "(same as source)" def _menu_callback(self): self._window.visible = not self._window.visible def _on_window(self, visible): if self._window.visible: self.build_ui() self._events = self._usd_context.get_stage_event_stream() self._stage_event_sub = self._events.create_subscription_to_pop( self._on_stage_event, name="urdf importer stage event" ) else: self._events = None self._stage_event_sub = None def _on_stage_event(self, event): stage = self._usd_context.get_stage() if event.type == int(omni.usd.StageEventType.OPENED) and stage: if UsdGeom.GetStageUpAxis(stage) == UsdGeom.Tokens.y: self._config.set_up_vector(0, 1, 0) if UsdGeom.GetStageUpAxis(stage) == UsdGeom.Tokens.z: self._config.set_up_vector(0, 0, 1) units_per_meter = 1.0 / UsdGeom.GetStageMetersPerUnit(stage) self._models["scale"].set_value(units_per_meter) self.dest_model.set_value(self.get_dest_folder()) def _load_robot(self, path=None): path = self._models["input_file"].get_value_as_string() if path: dest_path = self.dest_model.get_value_as_string() base_path = path[: path.rfind("/")] basename = path[path.rfind("/") + 1 :] basename = basename[: basename.rfind(".")] if path.rfind("/") < 0: base_path = path[: path.rfind("\\")] basename = path[path.rfind("\\") + 1] if dest_path != "(same as source)": base_path = dest_path # + "/" + basename dest_path = "{}/{}/{}.usd".format(base_path, basename, basename) # counter = 1 # while result[0] == Result.OK: # dest_path = "{}/{}_{:02}.usd".format(base_path, basename, counter) # result = omni.client.read_file(dest_path) # counter +=1 # result = omni.client.read_file(dest_path) # if # stage = Usd.Stage.Open(dest_path) # else: # stage = Usd.Stage.CreateNew(dest_path) # UsdGeom.SetStageUpAxis(stage, UsdGeom.Tokens.z) omni.kit.commands.execute( "URDFParseAndImportFile", urdf_path=path, import_config=self._config, dest_path=dest_path ) # print("Created file, instancing it now") stage = Usd.Stage.Open(dest_path) prim_name = str(stage.GetDefaultPrim().GetName()) # print(prim_name) # stage.Save() def add_reference_to_stage(): current_stage = omni.usd.get_context().get_stage() if current_stage: prim_path = omni.usd.get_stage_next_free_path( current_stage, str(current_stage.GetDefaultPrim().GetPath()) + "/" + prim_name, False ) robot_prim = current_stage.OverridePrim(prim_path) if "anon:" in current_stage.GetRootLayer().identifier: robot_prim.GetReferences().AddReference(dest_path) else: robot_prim.GetReferences().AddReference( omni.client.make_relative_url(current_stage.GetRootLayer().identifier, dest_path) ) if self._config.create_physics_scene: UsdPhysics.Scene.Define(current_stage, Sdf.Path("/physicsScene")) async def import_with_clean_stage(): await omni.usd.get_context().new_stage_async() await omni.kit.app.get_app().next_update_async() add_reference_to_stage() await omni.kit.app.get_app().next_update_async() if self._models["clean_stage"].get_value_as_bool(): asyncio.ensure_future(import_with_clean_stage()) else: add_reference_to_stage() def on_shutdown(self): _urdf.release_urdf_interface(self._urdf_interface) remove_menu_items(self._menu_items, "Isaac Utils") if self._window: self._window = None gc.collect()

Python

NVIDIA-Omniverse/urdf-importer-extension/source/extensions/omni.importer.urdf/python/scripts/ui/menu.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.ext from omni.kit.menu.utils import MenuItemDescription def make_menu_item_description(ext_id: str, name: str, onclick_fun, action_name: str = "") -> None: """Easily replace the onclick_fn with onclick_action when creating a menu description Args: ext_id (str): The extension you are adding the menu item to. name (str): Name of the menu item displayed in UI. onclick_fun (Function): The function to run when clicking the menu item. action_name (str): name for the action, in case ext_id+name don't make a unique string Note: ext_id + name + action_name must concatenate to a unique identifier. """ # TODO, fix errors when reloading extensions # action_unique = f'{ext_id.replace(" ", "_")}{name.replace(" ", "_")}{action_name.replace(" ", "_")}' # action_registry = omni.kit.actions.core.get_action_registry() # action_registry.register_action(ext_id, action_unique, onclick_fun) return MenuItemDescription(name=name, onclick_fn=onclick_fun)

Python

NVIDIA-Omniverse/urdf-importer-extension/source/extensions/omni.importer.urdf/python/scripts/ui/ui_utils.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import os import subprocess import sys from cmath import inf import carb.settings import omni.appwindow import omni.ext import omni.ui as ui from omni.kit.window.extensions import SimpleCheckBox from omni.kit.window.filepicker import FilePickerDialog from omni.kit.window.property.templates import LABEL_HEIGHT, LABEL_WIDTH # from .callbacks import on_copy_to_clipboard, on_docs_link_clicked, on_open_folder_clicked, on_open_IDE_clicked from .style import BUTTON_WIDTH, COLOR_W, COLOR_X, COLOR_Y, COLOR_Z, get_style def btn_builder(label="", type="button", text="button", tooltip="", on_clicked_fn=None): """Creates a stylized button. Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "button". text (str, optional): Text rendered on the button. Defaults to "button". tooltip (str, optional): Tooltip to display over the Label. Defaults to "". on_clicked_fn (Callable, optional): Call-back function when clicked. Defaults to None. Returns: ui.Button: Button """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) btn = ui.Button( text.upper(), name="Button", width=BUTTON_WIDTH, clicked_fn=on_clicked_fn, style=get_style(), alignment=ui.Alignment.LEFT_CENTER, ) ui.Spacer(width=5) add_line_rect_flourish(True) # ui.Spacer(width=ui.Fraction(1)) # ui.Spacer(width=10) # with ui.Frame(width=0): # with ui.VStack(): # with ui.Placer(offset_x=0, offset_y=7): # ui.Rectangle(height=5, width=5, alignment=ui.Alignment.CENTER) # ui.Spacer(width=5) return btn def state_btn_builder( label="", type="state_button", a_text="STATE A", b_text="STATE B", tooltip="", on_clicked_fn=None ): """Creates a State Change Button that changes text when pressed. Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "button". a_text (str, optional): Text rendered on the button for State A. Defaults to "STATE A". b_text (str, optional): Text rendered on the button for State B. Defaults to "STATE B". tooltip (str, optional): Tooltip to display over the Label. Defaults to "". on_clicked_fn (Callable, optional): Call-back function when clicked. Defaults to None. """ def toggle(): if btn.text == a_text.upper(): btn.text = b_text.upper() on_clicked_fn(True) else: btn.text = a_text.upper() on_clicked_fn(False) with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) btn = ui.Button( a_text.upper(), name="Button", width=BUTTON_WIDTH, clicked_fn=toggle, style=get_style(), alignment=ui.Alignment.LEFT_CENTER, ) ui.Spacer(width=5) # add_line_rect_flourish(False) ui.Spacer(width=ui.Fraction(1)) ui.Spacer(width=10) with ui.Frame(width=0): with ui.VStack(): with ui.Placer(offset_x=0, offset_y=7): ui.Rectangle(height=5, width=5, alignment=ui.Alignment.CENTER) ui.Spacer(width=5) return btn def cb_builder(label="", type="checkbox", default_val=False, tooltip="", on_clicked_fn=None): """Creates a Stylized Checkbox Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "checkbox". default_val (bool, optional): Checked is True, Unchecked is False. Defaults to False. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". on_clicked_fn (Callable, optional): Call-back function when clicked. Defaults to None. Returns: ui.SimpleBoolModel: model """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH - 12, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) model = ui.SimpleBoolModel() callable = on_clicked_fn if callable is None: callable = lambda x: None SimpleCheckBox(default_val, callable, model=model) add_line_rect_flourish() return model def multi_btn_builder( label="", type="multi_button", count=2, text=["button", "button"], tooltip=["", "", ""], on_clicked_fn=[None, None] ): """Creates a Row of Stylized Buttons Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "multi_button". count (int, optional): Number of UI elements to create. Defaults to 2. text (list, optional): List of text rendered on the UI elements. Defaults to ["button", "button"]. tooltip (list, optional): List of tooltips to display over the UI elements. Defaults to ["", "", ""]. on_clicked_fn (list, optional): List of call-backs function when clicked. Defaults to [None, None]. Returns: list(ui.Button): List of Buttons """ btns = [] with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip[0])) for i in range(count): btn = ui.Button( text[i].upper(), name="Button", width=BUTTON_WIDTH, clicked_fn=on_clicked_fn[i], tooltip=format_tt(tooltip[i + 1]), style=get_style(), alignment=ui.Alignment.LEFT_CENTER, ) btns.append(btn) if i < count: ui.Spacer(width=5) add_line_rect_flourish() return btns def multi_cb_builder( label="", type="multi_checkbox", count=2, text=[" ", " "], default_val=[False, False], tooltip=["", "", ""], on_clicked_fn=[None, None], ): """Creates a Row of Stylized Checkboxes. Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "multi_checkbox". count (int, optional): Number of UI elements to create. Defaults to 2. text (list, optional): List of text rendered on the UI elements. Defaults to [" ", " "]. default_val (list, optional): List of default values. Checked is True, Unchecked is False. Defaults to [False, False]. tooltip (list, optional): List of tooltips to display over the UI elements. Defaults to ["", "", ""]. on_clicked_fn (list, optional): List of call-backs function when clicked. Defaults to [None, None]. Returns: list(ui.SimpleBoolModel): List of models """ cbs = [] with ui.HStack(): ui.Label(label, width=LABEL_WIDTH - 12, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip[0])) for i in range(count): cb = ui.SimpleBoolModel(default_value=default_val[i]) callable = on_clicked_fn[i] if callable is None: callable = lambda x: None SimpleCheckBox(default_val[i], callable, model=cb) ui.Label( text[i], width=BUTTON_WIDTH / 2, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip[i + 1]) ) if i < count - 1: ui.Spacer(width=5) cbs.append(cb) add_line_rect_flourish() return cbs def str_builder( label="", type="stringfield", default_val=" ", tooltip="", on_clicked_fn=None, use_folder_picker=False, read_only=False, item_filter_fn=None, bookmark_label=None, bookmark_path=None, folder_dialog_title="Select Output Folder", folder_button_title="Select Folder", ): """Creates a Stylized Stringfield Widget Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "stringfield". default_val (str, optional): Text to initialize in Stringfield. Defaults to " ". tooltip (str, optional): Tooltip to display over the UI elements. Defaults to "". use_folder_picker (bool, optional): Add a folder picker button to the right. Defaults to False. read_only (bool, optional): Prevents editing. Defaults to False. item_filter_fn (Callable, optional): filter function to pass to the FilePicker bookmark_label (str, optional): bookmark label to pass to the FilePicker bookmark_path (str, optional): bookmark path to pass to the FilePicker Returns: AbstractValueModel: model of Stringfield """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) str_field = ui.StringField( name="StringField", width=ui.Fraction(1), height=0, alignment=ui.Alignment.LEFT_CENTER, read_only=read_only ).model str_field.set_value(default_val) if use_folder_picker: def update_field(filename, path): if filename == "": val = path elif filename[0] != "/" and path[-1] != "/": val = path + "/" + filename elif filename[0] == "/" and path[-1] == "/": val = path + filename[1:] else: val = path + filename str_field.set_value(val) add_folder_picker_icon( update_field, item_filter_fn, bookmark_label, bookmark_path, dialog_title=folder_dialog_title, button_title=folder_button_title, ) else: add_line_rect_flourish(False) return str_field def int_builder(label="", type="intfield", default_val=0, tooltip="", min=sys.maxsize * -1, max=sys.maxsize): """Creates a Stylized Intfield Widget Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "intfield". default_val (int, optional): Default Value of UI element. Defaults to 0. tooltip (str, optional): Tooltip to display over the UI elements. Defaults to "". min (int, optional): Minimum limit for int field. Defaults to sys.maxsize * -1 max (int, optional): Maximum limit for int field. Defaults to sys.maxsize * 1 Returns: AbstractValueModel: model """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) int_field = ui.IntDrag( name="Field", height=LABEL_HEIGHT, min=min, max=max, alignment=ui.Alignment.LEFT_CENTER ).model int_field.set_value(default_val) add_line_rect_flourish(False) return int_field def float_builder(label="", type="floatfield", default_val=0, tooltip="", min=-inf, max=inf, step=0.1, format="%.2f"): """Creates a Stylized Floatfield Widget Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "floatfield". default_val (int, optional): Default Value of UI element. Defaults to 0. tooltip (str, optional): Tooltip to display over the UI elements. Defaults to "". Returns: AbstractValueModel: model """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) float_field = ui.FloatDrag( name="FloatField", width=ui.Fraction(1), height=0, alignment=ui.Alignment.LEFT_CENTER, min=min, max=max, step=step, format=format, ).model float_field.set_value(default_val) add_line_rect_flourish(False) return float_field def combo_cb_str_builder( label="", type="checkbox_stringfield", default_val=[False, " "], tooltip="", on_clicked_fn=lambda x: None, use_folder_picker=False, read_only=False, folder_dialog_title="Select Output Folder", folder_button_title="Select Folder", ): """Creates a Stylized Checkbox + Stringfield Widget Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "checkbox_stringfield". default_val (str, optional): Text to initialize in Stringfield. Defaults to [False, " "]. tooltip (str, optional): Tooltip to display over the UI elements. Defaults to "". use_folder_picker (bool, optional): Add a folder picker button to the right. Defaults to False. read_only (bool, optional): Prevents editing. Defaults to False. Returns: Tuple(ui.SimpleBoolModel, AbstractValueModel): (cb_model, str_field_model) """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH - 12, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) cb = ui.SimpleBoolModel(default_value=default_val[0]) SimpleCheckBox(default_val[0], on_clicked_fn, model=cb) str_field = ui.StringField( name="StringField", width=ui.Fraction(1), height=0, alignment=ui.Alignment.LEFT_CENTER, read_only=read_only ).model str_field.set_value(default_val[1]) if use_folder_picker: def update_field(val): str_field.set_value(val) add_folder_picker_icon(update_field, dialog_title=folder_dialog_title, button_title=folder_button_title) else: add_line_rect_flourish(False) return cb, str_field def dropdown_builder( label="", type="dropdown", default_val=0, items=["Option 1", "Option 2", "Option 3"], tooltip="", on_clicked_fn=None ): """Creates a Stylized Dropdown Combobox Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "dropdown". default_val (int, optional): Default index of dropdown items. Defaults to 0. items (list, optional): List of items for dropdown box. Defaults to ["Option 1", "Option 2", "Option 3"]. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". on_clicked_fn (Callable, optional): Call-back function when clicked. Defaults to None. Returns: AbstractItemModel: model """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) combo_box = ui.ComboBox( default_val, *items, name="ComboBox", width=ui.Fraction(1), alignment=ui.Alignment.LEFT_CENTER ).model add_line_rect_flourish(False) def on_clicked_wrapper(model, val): on_clicked_fn(items[model.get_item_value_model().as_int]) if on_clicked_fn is not None: combo_box.add_item_changed_fn(on_clicked_wrapper) return combo_box def combo_intfield_slider_builder( label="", type="intfield_stringfield", default_val=0.5, min=0, max=1, step=0.01, tooltip=["", ""] ): """Creates a Stylized IntField + Stringfield Widget Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "intfield_stringfield". default_val (float, optional): Default Value. Defaults to 0.5. min (int, optional): Minimum Value. Defaults to 0. max (int, optional): Maximum Value. Defaults to 1. step (float, optional): Step. Defaults to 0.01. tooltip (list, optional): List of tooltips. Defaults to ["", ""]. Returns: Tuple(AbstractValueModel, IntSlider): (flt_field_model, flt_slider_model) """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip[0])) ff = ui.IntDrag( name="Field", width=BUTTON_WIDTH / 2, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip[1]) ).model ff.set_value(default_val) ui.Spacer(width=5) fs = ui.IntSlider( width=ui.Fraction(1), alignment=ui.Alignment.LEFT_CENTER, min=min, max=max, step=step, model=ff ) add_line_rect_flourish(False) return ff, fs def combo_floatfield_slider_builder( label="", type="floatfield_stringfield", default_val=0.5, min=0, max=1, step=0.01, tooltip=["", ""] ): """Creates a Stylized FloatField + FloatSlider Widget Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "floatfield_stringfield". default_val (float, optional): Default Value. Defaults to 0.5. min (int, optional): Minimum Value. Defaults to 0. max (int, optional): Maximum Value. Defaults to 1. step (float, optional): Step. Defaults to 0.01. tooltip (list, optional): List of tooltips. Defaults to ["", ""]. Returns: Tuple(AbstractValueModel, IntSlider): (flt_field_model, flt_slider_model) """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip[0])) ff = ui.FloatField( name="Field", width=BUTTON_WIDTH / 2, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip[1]) ).model ff.set_value(default_val) ui.Spacer(width=5) fs = ui.FloatSlider( width=ui.Fraction(1), alignment=ui.Alignment.LEFT_CENTER, min=min, max=max, step=step, model=ff ) add_line_rect_flourish(False) return ff, fs def multi_dropdown_builder( label="", type="multi_dropdown", count=2, default_val=[0, 0], items=[["Option 1", "Option 2", "Option 3"], ["Option A", "Option B", "Option C"]], tooltip="", on_clicked_fn=[None, None], ): """Creates a Stylized Multi-Dropdown Combobox Returns: AbstractItemModel: model Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "multi_dropdown". count (int, optional): Number of UI elements. Defaults to 2. default_val (list(int), optional): List of default indices of dropdown items. Defaults to 0.. Defaults to [0, 0]. items (list(list), optional): List of list of items for dropdown boxes. Defaults to [["Option 1", "Option 2", "Option 3"], ["Option A", "Option B", "Option C"]]. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". on_clicked_fn (list(Callable), optional): List of call-back function when clicked. Defaults to [None, None]. Returns: list(AbstractItemModel): list(models) """ elems = [] with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) for i in range(count): elem = ui.ComboBox( default_val[i], *items[i], name="ComboBox", width=ui.Fraction(1), alignment=ui.Alignment.LEFT_CENTER ) def on_clicked_wrapper(model, val, index): on_clicked_fn[index](items[index][model.get_item_value_model().as_int]) elem.model.add_item_changed_fn(lambda m, v, index=i: on_clicked_wrapper(m, v, index)) elems.append(elem) if i < count - 1: ui.Spacer(width=5) add_line_rect_flourish(False) return elems def combo_cb_dropdown_builder( label="", type="checkbox_dropdown", default_val=[False, 0], items=["Option 1", "Option 2", "Option 3"], tooltip="", on_clicked_fn=[lambda x: None, None], ): """Creates a Stylized Dropdown Combobox with an Enable Checkbox Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "checkbox_dropdown". default_val (list, optional): list(cb_default, dropdown_default). Defaults to [False, 0]. items (list, optional): List of items for dropdown box. Defaults to ["Option 1", "Option 2", "Option 3"]. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". on_clicked_fn (list, optional): List of callback functions. Defaults to [lambda x: None, None]. Returns: Tuple(ui.SimpleBoolModel, ui.ComboBox): (cb_model, combobox) """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH - 12, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) cb = ui.SimpleBoolModel(default_value=default_val[0]) SimpleCheckBox(default_val[0], on_clicked_fn[0], model=cb) combo_box = ui.ComboBox( default_val[1], *items, name="ComboBox", width=ui.Fraction(1), alignment=ui.Alignment.LEFT_CENTER ) def on_clicked_wrapper(model, val): on_clicked_fn[1](items[model.get_item_value_model().as_int]) combo_box.model.add_item_changed_fn(on_clicked_wrapper) add_line_rect_flourish(False) return cb, combo_box def scrolling_frame_builder(label="", type="scrolling_frame", default_val="No Data", tooltip=""): """Creates a Labeled Scrolling Frame with CopyToClipboard button Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "scrolling_frame". default_val (str, optional): Default Text. Defaults to "No Data". tooltip (str, optional): Tooltip to display over the Label. Defaults to "". Returns: ui.Label: label """ with ui.VStack(style=get_style(), spacing=5): with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_TOP, tooltip=format_tt(tooltip)) with ui.ScrollingFrame( height=LABEL_HEIGHT * 5, style_type_name_override="ScrollingFrame", alignment=ui.Alignment.LEFT_TOP, horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, vertical_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, ): text = ui.Label( default_val, style_type_name_override="Label::label", word_wrap=True, alignment=ui.Alignment.LEFT_TOP, ) with ui.Frame(width=0, tooltip="Copy To Clipboard"): ui.Button( name="IconButton", width=20, height=20, clicked_fn=lambda: on_copy_to_clipboard(to_copy=text.text), style=get_style()["IconButton.Image::CopyToClipboard"], alignment=ui.Alignment.RIGHT_TOP, ) return text def combo_cb_scrolling_frame_builder( label="", type="cb_scrolling_frame", default_val=[False, "No Data"], tooltip="", on_clicked_fn=lambda x: None ): """Creates a Labeled, Checkbox-enabled Scrolling Frame with CopyToClipboard button Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "cb_scrolling_frame". default_val (list, optional): List of Checkbox and Frame Defaults. Defaults to [False, "No Data"]. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". on_clicked_fn (Callable, optional): Callback function when clicked. Defaults to lambda x : None. Returns: list(SimpleBoolModel, ui.Label): (model, label) """ with ui.VStack(style=get_style(), spacing=5): with ui.HStack(): ui.Label(label, width=LABEL_WIDTH - 12, alignment=ui.Alignment.LEFT_TOP, tooltip=format_tt(tooltip)) with ui.VStack(width=0): cb = ui.SimpleBoolModel(default_value=default_val[0]) SimpleCheckBox(default_val[0], on_clicked_fn, model=cb) ui.Spacer(height=18 * 4) with ui.ScrollingFrame( height=18 * 5, style_type_name_override="ScrollingFrame", alignment=ui.Alignment.LEFT_TOP, horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, vertical_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, ): text = ui.Label( default_val[1], style_type_name_override="Label::label", word_wrap=True, alignment=ui.Alignment.LEFT_TOP, ) with ui.Frame(width=0, tooltip="Copy to Clipboard"): ui.Button( name="IconButton", width=20, height=20, clicked_fn=lambda: on_copy_to_clipboard(to_copy=text.text), style=get_style()["IconButton.Image::CopyToClipboard"], alignment=ui.Alignment.RIGHT_TOP, ) return cb, text def xyz_builder( label="", tooltip="", axis_count=3, default_val=[0.0, 0.0, 0.0, 0.0], min=float("-inf"), max=float("inf"), step=0.001, on_value_changed_fn=[None, None, None, None], ): """[summary] Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "". axis_count (int, optional): Number of Axes to Display. Max 4. Defaults to 3. default_val (list, optional): List of default values. Defaults to [0.0, 0.0, 0.0, 0.0]. min (float, optional): Minimum Float Value. Defaults to float("-inf"). max (float, optional): Maximum Float Value. Defaults to float("inf"). step (float, optional): Step. Defaults to 0.001. on_value_changed_fn (list, optional): List of callback functions for each axes. Defaults to [None, None, None, None]. Returns: list(AbstractValueModel): list(model) """ # These styles & colors are taken from omni.kit.property.transform_builder.py _create_multi_float_drag_matrix_with_labels if axis_count <= 0 or axis_count > 4: import builtins carb.log_warn("Invalid axis_count: must be in range 1 to 4. Clamping to default range.") axis_count = builtins.max(builtins.min(axis_count, 4), 1) field_labels = [("X", COLOR_X), ("Y", COLOR_Y), ("Z", COLOR_Z), ("W", COLOR_W)] field_tooltips = ["X Value", "Y Value", "Z Value", "W Value"] RECT_WIDTH = 13 # SPACING = 4 val_models = [None] * axis_count with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) with ui.ZStack(): with ui.HStack(): ui.Spacer(width=RECT_WIDTH) for i in range(axis_count): val_models[i] = ui.FloatDrag( name="Field", height=LABEL_HEIGHT, min=min, max=max, step=step, alignment=ui.Alignment.LEFT_CENTER, tooltip=field_tooltips[i], ).model val_models[i].set_value(default_val[i]) if on_value_changed_fn[i] is not None: val_models[i].add_value_changed_fn(on_value_changed_fn[i]) if i != axis_count - 1: ui.Spacer(width=19) with ui.HStack(): for i in range(axis_count): if i != 0: ui.Spacer() # width=BUTTON_WIDTH - 1) field_label = field_labels[i] with ui.ZStack(width=RECT_WIDTH + 2 * i): ui.Rectangle(name="vector_label", style={"background_color": field_label[1]}) ui.Label(field_label[0], name="vector_label", alignment=ui.Alignment.CENTER) ui.Spacer() add_line_rect_flourish(False) return val_models def color_picker_builder(label="", type="color_picker", default_val=[1.0, 1.0, 1.0, 1.0], tooltip="Color Picker"): """Creates a Color Picker Widget Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "color_picker". default_val (list, optional): List of (R,G,B,A) default values. Defaults to [1.0, 1.0, 1.0, 1.0]. tooltip (str, optional): Tooltip to display over the Label. Defaults to "Color Picker". Returns: AbstractItemModel: ui.ColorWidget.model """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER, tooltip=format_tt(tooltip)) model = ui.ColorWidget(*default_val, width=BUTTON_WIDTH).model ui.Spacer(width=5) add_line_rect_flourish() return model def progress_bar_builder(label="", type="progress_bar", default_val=0, tooltip="Progress"): """Creates a Progress Bar Widget Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "progress_bar". default_val (int, optional): Starting Value. Defaults to 0. tooltip (str, optional): Tooltip to display over the Label. Defaults to "Progress". Returns: AbstractValueModel: ui.ProgressBar().model """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_CENTER) model = ui.ProgressBar().model model.set_value(default_val) add_line_rect_flourish(False) return model def plot_builder(label="", data=None, min=-1, max=1, type=ui.Type.LINE, value_stride=1, color=None, tooltip=""): """Creates a stylized static plot Args: label (str, optional): Label to the left of the UI element. Defaults to "". data (list(float), optional): Data to plot. Defaults to None. min (int, optional): Minimum Y Value. Defaults to -1. max (int, optional): Maximum Y Value. Defaults to 1. type (ui.Type, optional): Plot Type. Defaults to ui.Type.LINE. value_stride (int, optional): Width of plot stride. Defaults to 1. color (int, optional): Plot color. Defaults to None. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". Returns: ui.Plot: plot """ with ui.VStack(spacing=5): with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_TOP, tooltip=format_tt(tooltip)) plot_height = LABEL_HEIGHT * 2 + 13 plot_width = ui.Fraction(1) with ui.ZStack(): ui.Rectangle(width=plot_width, height=plot_height) if not color: color = 0xFFDDDDDD plot = ui.Plot( type, min, max, *data, value_stride=value_stride, width=plot_width, height=plot_height, style={"color": color, "background_color": 0x0}, ) def update_min(model): plot.scale_min = model.as_float def update_max(model): plot.scale_max = model.as_float ui.Spacer(width=5) with ui.Frame(width=0): with ui.VStack(spacing=5): max_model = ui.FloatDrag( name="Field", width=40, alignment=ui.Alignment.LEFT_BOTTOM, tooltip="Max" ).model max_model.set_value(max) min_model = ui.FloatDrag( name="Field", width=40, alignment=ui.Alignment.LEFT_TOP, tooltip="Min" ).model min_model.set_value(min) min_model.add_value_changed_fn(update_min) max_model.add_value_changed_fn(update_max) ui.Spacer(width=20) add_separator() return plot def xyz_plot_builder(label="", data=[], min=-1, max=1, tooltip=""): """Creates a stylized static XYZ plot Args: label (str, optional): Label to the left of the UI element. Defaults to "". data (list(float), optional): Data to plot. Defaults to []. min (int, optional): Minimum Y Value. Defaults to -1. max (int, optional): Maximum Y Value. Defaults to "". tooltip (str, optional): Tooltip to display over the Label.. Defaults to "". Returns: list(ui.Plot): list(x_plot, y_plot, z_plot) """ with ui.VStack(spacing=5): with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_TOP, tooltip=format_tt(tooltip)) plot_height = LABEL_HEIGHT * 2 + 13 plot_width = ui.Fraction(1) with ui.ZStack(): ui.Rectangle(width=plot_width, height=plot_height) plot_0 = ui.Plot( ui.Type.LINE, min, max, *data[0], width=plot_width, height=plot_height, style=get_style()["PlotLabel::X"], ) plot_1 = ui.Plot( ui.Type.LINE, min, max, *data[1], width=plot_width, height=plot_height, style=get_style()["PlotLabel::Y"], ) plot_2 = ui.Plot( ui.Type.LINE, min, max, *data[2], width=plot_width, height=plot_height, style=get_style()["PlotLabel::Z"], ) def update_min(model): plot_0.scale_min = model.as_float plot_1.scale_min = model.as_float plot_2.scale_min = model.as_float def update_max(model): plot_0.scale_max = model.as_float plot_1.scale_max = model.as_float plot_2.scale_max = model.as_float ui.Spacer(width=5) with ui.Frame(width=0): with ui.VStack(spacing=5): max_model = ui.FloatDrag( name="Field", width=40, alignment=ui.Alignment.LEFT_BOTTOM, tooltip="Max" ).model max_model.set_value(max) min_model = ui.FloatDrag( name="Field", width=40, alignment=ui.Alignment.LEFT_TOP, tooltip="Min" ).model min_model.set_value(min) min_model.add_value_changed_fn(update_min) max_model.add_value_changed_fn(update_max) ui.Spacer(width=20) add_separator() return [plot_0, plot_1, plot_2] def combo_cb_plot_builder( label="", default_val=False, on_clicked_fn=lambda x: None, data=None, min=-1, max=1, type=ui.Type.LINE, value_stride=1, color=None, tooltip="", ): """Creates a Checkbox-Enabled dyanamic plot Args: label (str, optional): Label to the left of the UI element. Defaults to "". default_val (bool, optional): Checkbox default. Defaults to False. on_clicked_fn (Callable, optional): Checkbox Callback function. Defaults to lambda x: None. data (list(), optional): Data to plat. Defaults to None. min (int, optional): Min Y Value. Defaults to -1. max (int, optional): Max Y Value. Defaults to 1. type (ui.Type, optional): Plot Type. Defaults to ui.Type.LINE. value_stride (int, optional): Width of plot stride. Defaults to 1. color (int, optional): Plot color. Defaults to None. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". Returns: list(SimpleBoolModel, ui.Plot): (cb_model, plot) """ with ui.VStack(spacing=5): with ui.HStack(): # Label ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_TOP, tooltip=format_tt(tooltip)) # Checkbox with ui.Frame(width=0): with ui.Placer(offset_x=-10, offset_y=0): with ui.VStack(): SimpleCheckBox(default_val, on_clicked_fn) ui.Spacer(height=ui.Fraction(1)) ui.Spacer() # Plot plot_height = LABEL_HEIGHT * 2 + 13 plot_width = ui.Fraction(1) with ui.ZStack(): ui.Rectangle(width=plot_width, height=plot_height) if not color: color = 0xFFDDDDDD plot = ui.Plot( type, min, max, *data, value_stride=value_stride, width=plot_width, height=plot_height, style={"color": color, "background_color": 0x0}, ) # Min/Max Helpers def update_min(model): plot.scale_min = model.as_float def update_max(model): plot.scale_max = model.as_float ui.Spacer(width=5) with ui.Frame(width=0): with ui.VStack(spacing=5): # Min/Max Fields max_model = ui.FloatDrag( name="Field", width=40, alignment=ui.Alignment.LEFT_BOTTOM, tooltip="Max" ).model max_model.set_value(max) min_model = ui.FloatDrag( name="Field", width=40, alignment=ui.Alignment.LEFT_TOP, tooltip="Min" ).model min_model.set_value(min) min_model.add_value_changed_fn(update_min) max_model.add_value_changed_fn(update_max) ui.Spacer(width=20) with ui.HStack(): ui.Spacer(width=LABEL_WIDTH + 29) # Current Value Field (disabled by default) val_model = ui.FloatDrag( name="Field", width=BUTTON_WIDTH, height=LABEL_HEIGHT, enabled=False, alignment=ui.Alignment.LEFT_CENTER, tooltip="Value", ).model add_separator() return plot, val_model def combo_cb_xyz_plot_builder( label="", default_val=False, on_clicked_fn=lambda x: None, data=[], min=-1, max=1, type=ui.Type.LINE, value_stride=1, tooltip="", ): """[summary] Args: label (str, optional): Label to the left of the UI element. Defaults to "". default_val (bool, optional): Checkbox default. Defaults to False. on_clicked_fn (Callable, optional): Checkbox Callback function. Defaults to lambda x: None. data list(), optional): Data to plat. Defaults to None. min (int, optional): Min Y Value. Defaults to -1. max (int, optional): Max Y Value. Defaults to 1. type (ui.Type, optional): Plot Type. Defaults to ui.Type.LINE. value_stride (int, optional): Width of plot stride. Defaults to 1. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". Returns: Tuple(list(ui.Plot), list(AbstractValueModel)): ([plot_0, plot_1, plot_2], [val_model_x, val_model_y, val_model_z]) """ with ui.VStack(spacing=5): with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_TOP, tooltip=format_tt(tooltip)) # Checkbox with ui.Frame(width=0): with ui.Placer(offset_x=-10, offset_y=0): with ui.VStack(): SimpleCheckBox(default_val, on_clicked_fn) ui.Spacer(height=ui.Fraction(1)) ui.Spacer() # Plots plot_height = LABEL_HEIGHT * 2 + 13 plot_width = ui.Fraction(1) with ui.ZStack(): ui.Rectangle(width=plot_width, height=plot_height) plot_0 = ui.Plot( type, min, max, *data[0], value_stride=value_stride, width=plot_width, height=plot_height, style=get_style()["PlotLabel::X"], ) plot_1 = ui.Plot( type, min, max, *data[1], value_stride=value_stride, width=plot_width, height=plot_height, style=get_style()["PlotLabel::Y"], ) plot_2 = ui.Plot( type, min, max, *data[2], value_stride=value_stride, width=plot_width, height=plot_height, style=get_style()["PlotLabel::Z"], ) def update_min(model): plot_0.scale_min = model.as_float plot_1.scale_min = model.as_float plot_2.scale_min = model.as_float def update_max(model): plot_0.scale_max = model.as_float plot_1.scale_max = model.as_float plot_2.scale_max = model.as_float ui.Spacer(width=5) with ui.Frame(width=0): with ui.VStack(spacing=5): max_model = ui.FloatDrag( name="Field", width=40, alignment=ui.Alignment.LEFT_BOTTOM, tooltip="Max" ).model max_model.set_value(max) min_model = ui.FloatDrag( name="Field", width=40, alignment=ui.Alignment.LEFT_TOP, tooltip="Min" ).model min_model.set_value(min) min_model.add_value_changed_fn(update_min) max_model.add_value_changed_fn(update_max) ui.Spacer(width=20) # with ui.HStack(): # ui.Spacer(width=40) # val_models = xyz_builder()#**{"args":args}) field_labels = [("X", COLOR_X), ("Y", COLOR_Y), ("Z", COLOR_Z), ("W", COLOR_W)] RECT_WIDTH = 13 # SPACING = 4 with ui.HStack(): ui.Spacer(width=LABEL_WIDTH + 29) with ui.ZStack(): with ui.HStack(): ui.Spacer(width=RECT_WIDTH) # value_widget = ui.MultiFloatDragField( # *args, name="multivalue", min=min, max=max, step=step, h_spacing=RECT_WIDTH + SPACING, v_spacing=2 # ).model val_model_x = ui.FloatDrag( name="Field", width=BUTTON_WIDTH - 5, height=LABEL_HEIGHT, enabled=False, alignment=ui.Alignment.LEFT_CENTER, tooltip="X Value", ).model ui.Spacer(width=19) val_model_y = ui.FloatDrag( name="Field", width=BUTTON_WIDTH - 5, height=LABEL_HEIGHT, enabled=False, alignment=ui.Alignment.LEFT_CENTER, tooltip="Y Value", ).model ui.Spacer(width=19) val_model_z = ui.FloatDrag( name="Field", width=BUTTON_WIDTH - 5, height=LABEL_HEIGHT, enabled=False, alignment=ui.Alignment.LEFT_CENTER, tooltip="Z Value", ).model with ui.HStack(): for i in range(3): if i != 0: ui.Spacer(width=BUTTON_WIDTH - 1) field_label = field_labels[i] with ui.ZStack(width=RECT_WIDTH + 1): ui.Rectangle(name="vector_label", style={"background_color": field_label[1]}) ui.Label(field_label[0], name="vector_label", alignment=ui.Alignment.CENTER) add_separator() return [plot_0, plot_1, plot_2], [val_model_x, val_model_y, val_model_z] def add_line_rect_flourish(draw_line=True): """Aesthetic element that adds a Line + Rectangle after all UI elements in the row. Args: draw_line (bool, optional): Set false to only draw rectangle. Defaults to True. """ if draw_line: ui.Line(style={"color": 0x338A8777}, width=ui.Fraction(1), alignment=ui.Alignment.CENTER) ui.Spacer(width=10) with ui.Frame(width=0): with ui.VStack(): with ui.Placer(offset_x=0, offset_y=7): ui.Rectangle(height=5, width=5, alignment=ui.Alignment.CENTER) ui.Spacer(width=5) def add_separator(): """Aesthetic element to adds a Line Separator.""" with ui.VStack(spacing=5): ui.Spacer() with ui.HStack(): ui.Spacer(width=LABEL_WIDTH) ui.Line(style={"color": 0x338A8777}, width=ui.Fraction(1)) ui.Spacer(width=20) ui.Spacer() def add_folder_picker_icon( on_click_fn, item_filter_fn=None, bookmark_label=None, bookmark_path=None, dialog_title="Select Output Folder", button_title="Select Folder", ): def open_file_picker(): def on_selected(filename, path): on_click_fn(filename, path) file_picker.hide() def on_canceled(a, b): file_picker.hide() file_picker = FilePickerDialog( dialog_title, allow_multi_selection=False, apply_button_label=button_title, click_apply_handler=lambda a, b: on_selected(a, b), click_cancel_handler=lambda a, b: on_canceled(a, b), item_filter_fn=item_filter_fn, enable_versioning_pane=True, ) if bookmark_label and bookmark_path: file_picker.toggle_bookmark_from_path(bookmark_label, bookmark_path, True) with ui.Frame(width=0, tooltip=button_title): ui.Button( name="IconButton", width=24, height=24, clicked_fn=open_file_picker, style=get_style()["IconButton.Image::FolderPicker"], alignment=ui.Alignment.RIGHT_TOP, ) def add_folder_picker_btn(on_click_fn): def open_folder_picker(): def on_selected(a, b): on_click_fn(a, b) folder_picker.hide() def on_canceled(a, b): folder_picker.hide() folder_picker = FilePickerDialog( "Select Output Folder", allow_multi_selection=False, apply_button_label="Select Folder", click_apply_handler=lambda a, b: on_selected(a, b), click_cancel_handler=lambda a, b: on_canceled(a, b), ) with ui.Frame(width=0): ui.Button("SELECT", width=BUTTON_WIDTH, clicked_fn=open_folder_picker, tooltip="Select Folder") def format_tt(tt): import string formated = "" i = 0 for w in tt.split(): if w.isupper(): formated += w + " " elif len(w) > 3 or i == 0: formated += string.capwords(w) + " " else: formated += w.lower() + " " i += 1 return formated def setup_ui_headers( ext_id, file_path, title="My Custom Extension", doc_link="https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html", overview="", ): """Creates the Standard UI Elements at the top of each Isaac Extension. Args: ext_id (str): Extension ID. file_path (str): File path to source code. title (str, optional): Name of Extension. Defaults to "My Custom Extension". doc_link (str, optional): Hyperlink to Documentation. Defaults to "https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html". overview (str, optional): Overview Text explaining the Extension. Defaults to "". """ ext_manager = omni.kit.app.get_app().get_extension_manager() extension_path = ext_manager.get_extension_path(ext_id) ext_path = os.path.dirname(extension_path) if os.path.isfile(extension_path) else extension_path build_header(ext_path, file_path, title, doc_link) build_info_frame(overview) def build_header( ext_path, file_path, title="My Custom Extension", doc_link="https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html", ): """Title Header with Quick Access Utility Buttons.""" def build_icon_bar(): """Adds the Utility Buttons to the Title Header""" with ui.Frame(style=get_style(), width=0): with ui.VStack(): with ui.HStack(): icon_size = 24 with ui.Frame(tooltip="Open Source Code"): ui.Button( name="IconButton", width=icon_size, height=icon_size, clicked_fn=lambda: on_open_IDE_clicked(ext_path, file_path), style=get_style()["IconButton.Image::OpenConfig"], # style_type_name_override="IconButton.Image::OpenConfig", alignment=ui.Alignment.LEFT_CENTER, # tooltip="Open in IDE", ) with ui.Frame(tooltip="Open Containing Folder"): ui.Button( name="IconButton", width=icon_size, height=icon_size, clicked_fn=lambda: on_open_folder_clicked(file_path), style=get_style()["IconButton.Image::OpenFolder"], alignment=ui.Alignment.LEFT_CENTER, ) with ui.Placer(offset_x=0, offset_y=3): with ui.Frame(tooltip="Link to Docs"): ui.Button( name="IconButton", width=icon_size - icon_size * 0.25, height=icon_size - icon_size * 0.25, clicked_fn=lambda: on_docs_link_clicked(doc_link), style=get_style()["IconButton.Image::OpenLink"], alignment=ui.Alignment.LEFT_TOP, ) with ui.ZStack(): ui.Rectangle(style={"border_radius": 5}) with ui.HStack(): ui.Spacer(width=5) ui.Label(title, width=0, name="title", style={"font_size": 16}) ui.Spacer(width=ui.Fraction(1)) build_icon_bar() ui.Spacer(width=5) def build_info_frame(overview=""): """Info Frame with Overview, Instructions, and Metadata for an Extension""" frame = ui.CollapsableFrame( title="Information", height=0, collapsed=True, horizontal_clipping=False, style=get_style(), style_type_name_override="CollapsableFrame", horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, vertical_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, ) with frame: label = "Overview" default_val = overview tooltip = "Overview" with ui.VStack(style=get_style(), spacing=5): with ui.HStack(): ui.Label(label, width=LABEL_WIDTH / 2, alignment=ui.Alignment.LEFT_TOP, tooltip=format_tt(tooltip)) with ui.ScrollingFrame( height=LABEL_HEIGHT * 5, style_type_name_override="ScrollingFrame", alignment=ui.Alignment.LEFT_TOP, horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, vertical_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, ): text = ui.Label( default_val, style_type_name_override="Label::label", word_wrap=True, alignment=ui.Alignment.LEFT_TOP, ) with ui.Frame(width=0, tooltip="Copy To Clipboard"): ui.Button( name="IconButton", width=20, height=20, clicked_fn=lambda: on_copy_to_clipboard(to_copy=text.text), style=get_style()["IconButton.Image::CopyToClipboard"], alignment=ui.Alignment.RIGHT_TOP, ) return # def build_settings_frame(log_filename="extension.log", log_to_file=False, save_settings=False): # """Settings Frame for Common Utilities Functions""" # frame = ui.CollapsableFrame( # title="Settings", # height=0, # collapsed=True, # horizontal_clipping=False, # style=get_style(), # style_type_name_override="CollapsableFrame", # ) # def on_log_to_file_enabled(val): # # TO DO # carb.log_info(f"Logging to {model.get_value_as_string()}:", val) # def on_save_out_settings(val): # # TO DO # carb.log_info("Save Out Settings?", val) # with frame: # with ui.VStack(style=get_style(), spacing=5): # # # Log to File Settings # # default_output_path = os.path.realpath(os.getcwd()) # # kwargs = { # # "label": "Log to File", # # "type": "checkbox_stringfield", # # "default_val": [log_to_file, default_output_path + "/" + log_filename], # # "on_clicked_fn": on_log_to_file_enabled, # # "tooltip": "Log Out to File", # # "use_folder_picker": True, # # } # # model = combo_cb_str_builder(**kwargs)[1] # # Save Settings on Exit # # kwargs = { # # "label": "Save Settings", # # "type": "checkbox", # # "default_val": save_settings, # # "on_clicked_fn": on_save_out_settings, # # "tooltip": "Save out GUI Settings on Exit.", # # } # # cb_builder(**kwargs) class SearchListItem(ui.AbstractItem): def __init__(self, text): super().__init__() self.name_model = ui.SimpleStringModel(text) def __repr__(self): return f'"{self.name_model.as_string}"' def name(self): return self.name_model.as_string class SearchListItemModel(ui.AbstractItemModel): """ Represents the model for lists. It's very easy to initialize it with any string list: string_list = ["Hello", "World"] model = ListModel(*string_list) ui.TreeView(model) """ def __init__(self, *args): super().__init__() self._children = [SearchListItem(t) for t in args] self._filtered = [SearchListItem(t) for t in args] def get_item_children(self, item): """Returns all the children when the widget asks it.""" if item is not None: # Since we are doing a flat list, we return the children of root only. # If it's not root we return. return [] return self._filtered def filter_text(self, text): import fnmatch self._filtered = [] if len(text) == 0: for c in self._children: self._filtered.append(c) else: parts = text.split() # for i in range(len(parts) - 1, -1, -1): # w = parts[i] leftover = " ".join(parts) if len(leftover) > 0: filter_str = f"*{leftover.lower()}*" for c in self._children: if fnmatch.fnmatch(c.name().lower(), filter_str): self._filtered.append(c) # This tells the Delegate to update the TreeView self._item_changed(None) def get_item_value_model_count(self, item): """The number of columns""" return 1 def get_item_value_model(self, item, column_id): """ Return value model. It's the object that tracks the specific value. In our case we use ui.SimpleStringModel. """ return item.name_model class SearchListItemDelegate(ui.AbstractItemDelegate): """ Delegate is the representation layer. TreeView calls the methods of the delegate to create custom widgets for each item. """ def __init__(self, on_double_click_fn=None): super().__init__() self._on_double_click_fn = on_double_click_fn def build_branch(self, model, item, column_id, level, expanded): """Create a branch widget that opens or closes subtree""" pass def build_widget(self, model, item, column_id, level, expanded): """Create a widget per column per item""" stack = ui.ZStack(height=20, style=get_style()) with stack: with ui.HStack(): ui.Spacer(width=5) value_model = model.get_item_value_model(item, column_id) label = ui.Label(value_model.as_string, name="TreeView.Item") if not self._on_double_click_fn: self._on_double_click_fn = self.on_double_click # Set a double click function stack.set_mouse_double_clicked_fn(lambda x, y, b, m, l=label: self._on_double_click_fn(b, m, l)) def on_double_click(self, button, model, label): """Called when the user double-clicked the item in TreeView""" if button != 0: return def build_simple_search(label="", type="search", model=None, delegate=None, tooltip=""): """A Simple Search Bar + TreeView Widget.\n Pass a list of items through the model, and a custom on_click_fn through the delegate.\n Returns the SearchWidget so user can destroy it on_shutdown. Args: label (str, optional): Label to the left of the UI element. Defaults to "". type (str, optional): Type of UI element. Defaults to "search". model (ui.AbstractItemModel, optional): Item Model for Search. Defaults to None. delegate (ui.AbstractItemDelegate, optional): Item Delegate for Search. Defaults to None. tooltip (str, optional): Tooltip to display over the Label. Defaults to "". Returns: Tuple(Search Widget, Treeview): """ with ui.HStack(): ui.Label(label, width=LABEL_WIDTH, alignment=ui.Alignment.LEFT_TOP, tooltip=format_tt(tooltip)) with ui.VStack(spacing=5): def filter_text(item): model.filter_text(item) from omni.kit.window.extensions.ext_components import SearchWidget search_bar = SearchWidget(filter_text) with ui.ScrollingFrame( height=LABEL_HEIGHT * 5, horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_OFF, vertical_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, style=get_style(), style_type_name_override="TreeView.ScrollingFrame", ): treeview = ui.TreeView( model, delegate=delegate, root_visible=False, header_visible=False, style={ "TreeView.ScrollingFrame": {"background_color": 0xFFE0E0E0}, "TreeView.Item": {"color": 0xFF535354, "font_size": 16}, "TreeView.Item:selected": {"color": 0xFF23211F}, "TreeView:selected": {"background_color": 0x409D905C}, } # name="TreeView", # style_type_name_override="TreeView", ) add_line_rect_flourish(False) return search_bar, treeview

Python

NVIDIA-Omniverse/urdf-importer-extension/source/extensions/omni.importer.urdf/python/scripts/samples/common.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import carb.tokens import omni from pxr import PhysxSchema, UsdGeom, UsdPhysics def set_drive_parameters(drive, target_type, target_value, stiffness=None, damping=None, max_force=None): """Enable velocity drive for a given joint""" if target_type == "position": if not drive.GetTargetPositionAttr(): drive.CreateTargetPositionAttr(target_value) else: drive.GetTargetPositionAttr().Set(target_value) elif target_type == "velocity": if not drive.GetTargetVelocityAttr(): drive.CreateTargetVelocityAttr(target_value) else: drive.GetTargetVelocityAttr().Set(target_value) if stiffness is not None: if not drive.GetStiffnessAttr(): drive.CreateStiffnessAttr(stiffness) else: drive.GetStiffnessAttr().Set(stiffness) if damping is not None: if not drive.GetDampingAttr(): drive.CreateDampingAttr(damping) else: drive.GetDampingAttr().Set(damping) if max_force is not None: if not drive.GetMaxForceAttr(): drive.CreateMaxForceAttr(max_force) else: drive.GetMaxForceAttr().Set(max_force)

Python

NVIDIA-Omniverse/urdf-importer-extension/source/extensions/omni.importer.urdf/python/scripts/samples/import_franka.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import math import weakref import omni import omni.ui as ui from omni.importer.urdf.scripts.ui import ( btn_builder, get_style, make_menu_item_description, setup_ui_headers, ) from omni.kit.menu.utils import MenuItemDescription, add_menu_items, remove_menu_items from omni.kit.viewport.utility.camera_state import ViewportCameraState from pxr import Gf, PhysicsSchemaTools, PhysxSchema, Sdf, UsdLux, UsdPhysics from .common import set_drive_parameters EXTENSION_NAME = "Import Franka" class Extension(omni.ext.IExt): def on_startup(self, ext_id: str): ext_manager = omni.kit.app.get_app().get_extension_manager() self._ext_id = ext_id self._extension_path = ext_manager.get_extension_path(ext_id) self._menu_items = [ MenuItemDescription( name="Import Robots", sub_menu=[ make_menu_item_description(ext_id, "Franka URDF", lambda a=weakref.proxy(self): a._menu_callback()) ], ) ] add_menu_items(self._menu_items, "Isaac Examples") self._build_ui() def _build_ui(self): self._window = omni.ui.Window( EXTENSION_NAME, width=0, height=0, visible=False, dockPreference=ui.DockPreference.LEFT_BOTTOM ) with self._window.frame: with ui.VStack(spacing=5, height=0): title = "Import a Franka Panda via URDF" doc_link = "https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/ext_omni_isaac_urdf.html" overview = ( "This Example shows you import a URDF.\n\nPress the 'Open in IDE' button to view the source code." ) setup_ui_headers(self._ext_id, __file__, title, doc_link, overview) frame = ui.CollapsableFrame( title="Command Panel", height=0, collapsed=False, style=get_style(), style_type_name_override="CollapsableFrame", horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, vertical_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, ) with frame: with ui.VStack(style=get_style(), spacing=5): dict = { "label": "Load Robot", "type": "button", "text": "Load", "tooltip": "Load a UR10 Robot into the Scene", "on_clicked_fn": self._on_load_robot, } btn_builder(**dict) dict = { "label": "Configure Drives", "type": "button", "text": "Configure", "tooltip": "Configure Joint Drives", "on_clicked_fn": self._on_config_robot, } btn_builder(**dict) dict = { "label": "Move to Pose", "type": "button", "text": "move", "tooltip": "Drive the Robot to a specific pose", "on_clicked_fn": self._on_config_drives, } btn_builder(**dict) def on_shutdown(self): remove_menu_items(self._menu_items, "Isaac Examples") self._window = None def _menu_callback(self): self._window.visible = not self._window.visible def _on_load_robot(self): load_stage = asyncio.ensure_future(omni.usd.get_context().new_stage_async()) asyncio.ensure_future(self._load_franka(load_stage)) async def _load_franka(self, task): done, pending = await asyncio.wait({task}) if task in done: status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.merge_fixed_joints = False import_config.fix_base = True import_config.make_default_prim = True import_config.create_physics_scene = True omni.kit.commands.execute( "URDFParseAndImportFile", urdf_path=self._extension_path + "/data/urdf/robots/franka_description/robots/panda_arm_hand.urdf", import_config=import_config, ) camera_state = ViewportCameraState("/OmniverseKit_Persp") camera_state.set_position_world(Gf.Vec3d(1.22, -1.24, 1.13), True) camera_state.set_target_world(Gf.Vec3d(-0.96, 1.08, 0.0), True) stage = omni.usd.get_context().get_stage() scene = UsdPhysics.Scene.Define(stage, Sdf.Path("/physicsScene")) scene.CreateGravityDirectionAttr().Set(Gf.Vec3f(0.0, 0.0, -1.0)) scene.CreateGravityMagnitudeAttr().Set(9.81) plane_path = "/groundPlane" PhysicsSchemaTools.addGroundPlane( stage, plane_path, "Z", 1500.0, Gf.Vec3f(0, 0, 0), Gf.Vec3f([0.5, 0.5, 0.5]), ) # make sure the ground plane is under root prim and not robot omni.kit.commands.execute( "MovePrimCommand", path_from=plane_path, path_to="/groundPlane", keep_world_transform=True ) distantLight = UsdLux.DistantLight.Define(stage, Sdf.Path("/DistantLight")) distantLight.CreateIntensityAttr(500) def _on_config_robot(self): stage = omni.usd.get_context().get_stage() # Set the solver parameters on the articulation PhysxSchema.PhysxArticulationAPI.Get(stage, "/panda").CreateSolverPositionIterationCountAttr(64) PhysxSchema.PhysxArticulationAPI.Get(stage, "/panda").CreateSolverVelocityIterationCountAttr(64) self.joint_1 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/panda/panda_link0/panda_joint1"), "angular") self.joint_2 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/panda/panda_link1/panda_joint2"), "angular") self.joint_3 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/panda/panda_link2/panda_joint3"), "angular") self.joint_4 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/panda/panda_link3/panda_joint4"), "angular") self.joint_5 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/panda/panda_link4/panda_joint5"), "angular") self.joint_6 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/panda/panda_link5/panda_joint6"), "angular") self.joint_7 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/panda/panda_link6/panda_joint7"), "angular") self.finger_1 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/panda/panda_hand/panda_finger_joint1"), "linear") self.finger_2 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/panda/panda_hand/panda_finger_joint2"), "linear") # Set the drive mode, target, stiffness, damping and max force for each joint set_drive_parameters(self.joint_1, "position", math.degrees(0), math.radians(1e8), math.radians(1e7)) set_drive_parameters(self.joint_2, "position", math.degrees(0), math.radians(1e8), math.radians(1e7)) set_drive_parameters(self.joint_3, "position", math.degrees(0), math.radians(1e8), math.radians(1e7)) set_drive_parameters(self.joint_4, "position", math.degrees(0), math.radians(1e8), math.radians(1e7)) set_drive_parameters(self.joint_5, "position", math.degrees(0), math.radians(1e8), math.radians(1e7)) set_drive_parameters(self.joint_6, "position", math.degrees(0), math.radians(1e8), math.radians(1e7)) set_drive_parameters(self.joint_7, "position", math.degrees(0), math.radians(1e8), math.radians(1e7)) set_drive_parameters(self.finger_1, "position", 0, 1e7, 1e6) set_drive_parameters(self.finger_2, "position", 0, 1e7, 1e6) def _on_config_drives(self): self._on_config_robot() # make sure drives are configured first # Set the drive mode, target, stiffness, damping and max force for each joint set_drive_parameters(self.joint_1, "position", math.degrees(0.012)) set_drive_parameters(self.joint_2, "position", math.degrees(-0.57)) set_drive_parameters(self.joint_3, "position", math.degrees(0)) set_drive_parameters(self.joint_4, "position", math.degrees(-2.81)) set_drive_parameters(self.joint_5, "position", math.degrees(0)) set_drive_parameters(self.joint_6, "position", math.degrees(3.037)) set_drive_parameters(self.joint_7, "position", math.degrees(0.741)) set_drive_parameters(self.finger_1, "position", 4) set_drive_parameters(self.finger_2, "position", 4)

Python

NVIDIA-Omniverse/urdf-importer-extension/source/extensions/omni.importer.urdf/python/scripts/samples/import_kaya.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import math import weakref import omni import omni.kit.commands import omni.ui as ui from omni.importer.urdf.scripts.ui import ( btn_builder, get_style, make_menu_item_description, setup_ui_headers, ) from omni.kit.menu.utils import MenuItemDescription, add_menu_items, remove_menu_items from omni.kit.viewport.utility.camera_state import ViewportCameraState from pxr import Gf, PhysicsSchemaTools, Sdf, UsdLux, UsdPhysics from .common import set_drive_parameters EXTENSION_NAME = "Import Kaya" class Extension(omni.ext.IExt): def on_startup(self, ext_id: str): ext_manager = omni.kit.app.get_app().get_extension_manager() self._ext_id = ext_id self._extension_path = ext_manager.get_extension_path(ext_id) self._menu_items = [ MenuItemDescription( name="Import Robots", sub_menu=[ make_menu_item_description(ext_id, "Kaya URDF", lambda a=weakref.proxy(self): a._menu_callback()) ], ) ] add_menu_items(self._menu_items, "Isaac Examples") self._build_ui() def _build_ui(self): self._window = omni.ui.Window( EXTENSION_NAME, width=0, height=0, visible=False, dockPreference=ui.DockPreference.LEFT_BOTTOM ) with self._window.frame: with ui.VStack(spacing=5, height=0): title = "Import a Kaya Robot via URDF" doc_link = "https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/ext_omni_isaac_urdf.html" overview = "This Example shows you import an NVIDIA Kaya robot via URDF.\n\nPress the 'Open in IDE' button to view the source code." setup_ui_headers(self._ext_id, __file__, title, doc_link, overview) frame = ui.CollapsableFrame( title="Command Panel", height=0, collapsed=False, style=get_style(), style_type_name_override="CollapsableFrame", horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED, vertical_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON, ) with frame: with ui.VStack(style=get_style(), spacing=5): dict = { "label": "Load Robot", "type": "button", "text": "Load", "tooltip": "Load a UR10 Robot into the Scene", "on_clicked_fn": self._on_load_robot, } btn_builder(**dict) dict = { "label": "Configure Drives", "type": "button", "text": "Configure", "tooltip": "Configure Joint Drives", "on_clicked_fn": self._on_config_robot, } btn_builder(**dict) dict = { "label": "Spin Robot", "type": "button", "text": "move", "tooltip": "Spin the Robot in Place", "on_clicked_fn": self._on_config_drives, } btn_builder(**dict) def on_shutdown(self): remove_menu_items(self._menu_items, "Isaac Examples") self._window = None def _menu_callback(self): self._window.visible = not self._window.visible def _on_load_robot(self): load_stage = asyncio.ensure_future(omni.usd.get_context().new_stage_async()) asyncio.ensure_future(self._load_kaya(load_stage)) async def _load_kaya(self, task): done, pending = await asyncio.wait({task}) if task in done: status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.merge_fixed_joints = True import_config.import_inertia_tensor = False # import_config.distance_scale = 1.0 import_config.fix_base = False import_config.make_default_prim = True import_config.create_physics_scene = True omni.kit.commands.execute( "URDFParseAndImportFile", urdf_path=self._extension_path + "/data/urdf/robots/kaya/urdf/kaya.urdf", import_config=import_config, ) camera_state = ViewportCameraState("/OmniverseKit_Persp") camera_state.set_position_world(Gf.Vec3d(-1.0, 1.5, 0.5), True) camera_state.set_target_world(Gf.Vec3d(0.0, 0.0, 0.0), True) stage = omni.usd.get_context().get_stage() scene = UsdPhysics.Scene.Define(stage, Sdf.Path("/physicsScene")) scene.CreateGravityDirectionAttr().Set(Gf.Vec3f(0.0, 0.0, -1.0)) scene.CreateGravityMagnitudeAttr().Set(9.81) plane_path = "/groundPlane" PhysicsSchemaTools.addGroundPlane( stage, plane_path, "Z", 1500.0, Gf.Vec3f(0, 0, -0.25), Gf.Vec3f([0.5, 0.5, 0.5]) ) # make sure the ground plane is under root prim and not robot omni.kit.commands.execute( "MovePrimCommand", path_from=plane_path, path_to="/groundPlane", keep_world_transform=True ) distantLight = UsdLux.DistantLight.Define(stage, Sdf.Path("/DistantLight")) distantLight.CreateIntensityAttr(500) def _on_config_robot(self): stage = omni.usd.get_context().get_stage() # Make all rollers spin freely by removing extra drive API for axle in range(0, 2 + 1): for ring in range(0, 1 + 1): for roller in range(0, 4 + 1): prim_path = ( "/kaya/axle_" + str(axle) + "/roller_" + str(axle) + "_" + str(ring) + "_" + str(roller) + "_joint" ) prim = stage.GetPrimAtPath(prim_path) # omni.kit.commands.execute( # "UnapplyAPISchemaCommand", # api=UsdPhysics.DriveAPI, # prim=prim, # api_prefix="drive", # multiple_api_token="angular", # ) prim.RemoveAPI(UsdPhysics.DriveAPI, "angular") def _on_config_drives(self): self._on_config_robot() # make sure drives are configured first stage = omni.usd.get_context().get_stage() # set each axis to spin at a rate of 1 rad/s axle_0 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/kaya/base_link/axle_0_joint"), "angular") axle_1 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/kaya/base_link/axle_1_joint"), "angular") axle_2 = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/kaya/base_link/axle_2_joint"), "angular") set_drive_parameters(axle_0, "velocity", math.degrees(1), 0, math.radians(1e7)) set_drive_parameters(axle_1, "velocity", math.degrees(1), 0, math.radians(1e7)) set_drive_parameters(axle_2, "velocity", math.degrees(1), 0, math.radians(1e7))

Python

NVIDIA-Omniverse/urdf-importer-extension/source/extensions/omni.importer.urdf/python/tests/test_urdf.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import os import numpy as np import omni.kit.commands # NOTE: # omni.kit.test - std python's unittest module with additional wrapping to add suport for async/await tests # For most things refer to unittest docs: https://docs.python.org/3/library/unittest.html import omni.kit.test import pxr from pxr import Gf, PhysicsSchemaTools, Sdf, UsdGeom, UsdPhysics, UsdShade # Having a test class dervived from omni.kit.test.AsyncTestCase declared on the root of module will make it auto-discoverable by omni.kit.test class TestUrdf(omni.kit.test.AsyncTestCase): # Before running each test async def setUp(self): self._timeline = omni.timeline.get_timeline_interface() ext_manager = omni.kit.app.get_app().get_extension_manager() ext_id = ext_manager.get_enabled_extension_id("omni.importer.urdf") self._extension_path = ext_manager.get_extension_path(ext_id) self.dest_path = os.path.abspath(self._extension_path + "/tests_out") await omni.usd.get_context().new_stage_async() await omni.kit.app.get_app().next_update_async() pass # After running each test async def tearDown(self): # _urdf.release_urdf_interface(self._urdf_interface) await omni.kit.app.get_app().next_update_async() pass # Tests to make sure visual mesh names are incremented async def test_urdf_mesh_naming(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_names.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.merge_fixed_joints = True omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) prim = stage.GetPrimAtPath("/test_names/cube/visuals") prim_range = prim.GetChildren() # There should be a total of 6 visual meshes after import self.assertEqual(len(prim_range), 6) # basic urdf test: joints and links are imported correctly async def test_urdf_basic(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_basic.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.import_inertia_tensor = True omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) await omni.kit.app.get_app().next_update_async() prim = stage.GetPrimAtPath("/test_basic") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) # make sure the joints exist root_joint = stage.GetPrimAtPath("/test_basic/root_joint") self.assertNotEqual(root_joint.GetPath(), Sdf.Path.emptyPath) wristJoint = stage.GetPrimAtPath("/test_basic/link_2/wrist_joint") self.assertNotEqual(wristJoint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(wristJoint.GetTypeName(), "PhysicsRevoluteJoint") fingerJoint = stage.GetPrimAtPath("/test_basic/palm_link/finger_1_joint") self.assertNotEqual(fingerJoint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(fingerJoint.GetTypeName(), "PhysicsPrismaticJoint") self.assertAlmostEqual(fingerJoint.GetAttribute("physics:upperLimit").Get(), 0.08) fingerLink = stage.GetPrimAtPath("/test_basic/finger_link_2") self.assertAlmostEqual(fingerLink.GetAttribute("physics:diagonalInertia").Get()[0], 2.0) self.assertAlmostEqual(fingerLink.GetAttribute("physics:mass").Get(), 3) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() self.assertAlmostEqual(UsdGeom.GetStageMetersPerUnit(stage), 1.0) pass async def test_urdf_save_to_file(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_basic.urdf") dest_path = os.path.abspath(self.dest_path + "/test_basic.usd") status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.import_inertia_tensor = True omni.kit.commands.execute( "URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config, dest_path=dest_path ) await omni.kit.app.get_app().next_update_async() stage = pxr.Usd.Stage.Open(dest_path) prim = stage.GetPrimAtPath("/test_basic") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) # make sure the joints exist root_joint = stage.GetPrimAtPath("/test_basic/root_joint") self.assertNotEqual(root_joint.GetPath(), Sdf.Path.emptyPath) wristJoint = stage.GetPrimAtPath("/test_basic/link_2/wrist_joint") self.assertNotEqual(wristJoint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(wristJoint.GetTypeName(), "PhysicsRevoluteJoint") fingerJoint = stage.GetPrimAtPath("/test_basic/palm_link/finger_1_joint") self.assertNotEqual(fingerJoint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(fingerJoint.GetTypeName(), "PhysicsPrismaticJoint") self.assertAlmostEqual(fingerJoint.GetAttribute("physics:upperLimit").Get(), 0.08) fingerLink = stage.GetPrimAtPath("/test_basic/finger_link_2") self.assertAlmostEqual(fingerLink.GetAttribute("physics:diagonalInertia").Get()[0], 2.0) self.assertAlmostEqual(fingerLink.GetAttribute("physics:mass").Get(), 3) self.assertAlmostEqual(UsdGeom.GetStageMetersPerUnit(stage), 1.0) stage = None pass async def test_urdf_textured_obj(self): base_path = self._extension_path + "/data/urdf/tests/test_textures_urdf" basename = "cube_obj" dest_path = "{}/{}/{}.usd".format(self.dest_path, basename, basename) mats_path = "{}/{}/materials".format(self.dest_path, basename) omni.client.create_folder("{}/{}".format(self.dest_path, basename)) omni.client.create_folder(mats_path) urdf_path = "{}/{}.urdf".format(base_path, basename) status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") omni.kit.commands.execute( "URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config, dest_path=dest_path ) await omni.kit.app.get_app().next_update_async() result = omni.client.list(mats_path) self.assertEqual(result[0], omni.client._omniclient.Result.OK) self.assertEqual(len(result[1]), 4) # Metallic texture is unsuported by assimp on OBJ pass async def test_urdf_textured_in_memory(self): base_path = self._extension_path + "/data/urdf/tests/test_textures_urdf" basename = "cube_obj" urdf_path = "{}/{}.urdf".format(base_path, basename) status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) await omni.kit.app.get_app().next_update_async() pass async def test_urdf_textured_dae(self): base_path = self._extension_path + "/data/urdf/tests/test_textures_urdf" basename = "cube_dae" dest_path = "{}/{}/{}.usd".format(self.dest_path, basename, basename) mats_path = "{}/{}/materials".format(self.dest_path, basename) omni.client.create_folder("{}/{}".format(self.dest_path, basename)) omni.client.create_folder(mats_path) urdf_path = "{}/{}.urdf".format(base_path, basename) status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") omni.kit.commands.execute( "URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config, dest_path=dest_path ) await omni.kit.app.get_app().next_update_async() result = omni.client.list(mats_path) self.assertEqual(result[0], omni.client._omniclient.Result.OK) self.assertEqual(len(result[1]), 1) # only albedo is supported for Collada pass async def test_urdf_overwrite_file(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_basic.urdf") dest_path = os.path.abspath(self._extension_path + "/data/urdf/tests/tests_out/test_basic.usd") status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.import_inertia_tensor = True omni.kit.commands.execute( "URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config, dest_path=dest_path ) await omni.kit.app.get_app().next_update_async() omni.kit.commands.execute( "URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config, dest_path=dest_path ) await omni.kit.app.get_app().next_update_async() stage = pxr.Usd.Stage.Open(dest_path) prim = stage.GetPrimAtPath("/test_basic") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) # make sure the joints exist root_joint = stage.GetPrimAtPath("/test_basic/root_joint") self.assertNotEqual(root_joint.GetPath(), Sdf.Path.emptyPath) wristJoint = stage.GetPrimAtPath("/test_basic/link_2/wrist_joint") self.assertNotEqual(wristJoint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(wristJoint.GetTypeName(), "PhysicsRevoluteJoint") fingerJoint = stage.GetPrimAtPath("/test_basic/palm_link/finger_1_joint") self.assertNotEqual(fingerJoint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(fingerJoint.GetTypeName(), "PhysicsPrismaticJoint") self.assertAlmostEqual(fingerJoint.GetAttribute("physics:upperLimit").Get(), 0.08) fingerLink = stage.GetPrimAtPath("/test_basic/finger_link_2") self.assertAlmostEqual(fingerLink.GetAttribute("physics:diagonalInertia").Get()[0], 2.0) self.assertAlmostEqual(fingerLink.GetAttribute("physics:mass").Get(), 3) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() self.assertAlmostEqual(UsdGeom.GetStageMetersPerUnit(stage), 1.0) stage = None pass # advanced urdf test: test for all the categories of inputs that an urdf can hold async def test_urdf_advanced(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_advanced.urdf") stage = omni.usd.get_context().get_stage() # enable merging fixed joints status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.merge_fixed_joints = True import_config.default_position_drive_damping = -1 # ignore this setting by making it -1 omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) await omni.kit.app.get_app().next_update_async() # check if object is there prim = stage.GetPrimAtPath("/test_advanced") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) # check color are imported mesh = stage.GetPrimAtPath("/test_advanced/link_1/visuals") self.assertNotEqual(mesh.GetPath(), Sdf.Path.emptyPath) mat, rel = UsdShade.MaterialBindingAPI(mesh).ComputeBoundMaterial() shader = UsdShade.Shader(stage.GetPrimAtPath(mat.GetPath().pathString + "/Shader")) self.assertTrue(Gf.IsClose(shader.GetInput("diffuse_color_constant").Get(), Gf.Vec3f(0, 0.8, 0), 1e-5)) # check joint properties elbowPrim = stage.GetPrimAtPath("/test_advanced/link_1/elbow_joint") self.assertNotEqual(elbowPrim.GetPath(), Sdf.Path.emptyPath) self.assertAlmostEqual(elbowPrim.GetAttribute("physxJoint:jointFriction").Get(), 0.1) self.assertAlmostEqual(elbowPrim.GetAttribute("drive:angular:physics:damping").Get(), 0.1) # check position of a link joint_pos = elbowPrim.GetAttribute("physics:localPos0").Get() self.assertTrue(Gf.IsClose(joint_pos, Gf.Vec3f(0, 0, 0.40), 1e-5)) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() pass # test for importing urdf where fixed joints are merged async def test_urdf_merge_joints(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_merge_joints.urdf") stage = omni.usd.get_context().get_stage() # enable merging fixed joints status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.merge_fixed_joints = True omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) # the merged link shouldn't be there prim = stage.GetPrimAtPath("/test_merge_joints/link_2") self.assertEqual(prim.GetPath(), Sdf.Path.emptyPath) pass async def test_urdf_mtl(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_mtl.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) mesh = stage.GetPrimAtPath("/test_mtl/cube/visuals") self.assertTrue(UsdShade.MaterialBindingAPI(mesh) is not None) mat, rel = UsdShade.MaterialBindingAPI(mesh).ComputeBoundMaterial() shader = UsdShade.Shader(stage.GetPrimAtPath(mat.GetPath().pathString + "/Shader")) print(shader) self.assertTrue(Gf.IsClose(shader.GetInput("diffuse_color_constant").Get(), Gf.Vec3f(0.8, 0.0, 0), 1e-5)) async def test_urdf_material(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_material.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) mesh = stage.GetPrimAtPath("/test_material/base/visuals") self.assertTrue(UsdShade.MaterialBindingAPI(mesh) is not None) mat, rel = UsdShade.MaterialBindingAPI(mesh).ComputeBoundMaterial() shader = UsdShade.Shader(stage.GetPrimAtPath(mat.GetPath().pathString + "/Shader")) print(shader) self.assertTrue(Gf.IsClose(shader.GetInput("diffuse_color_constant").Get(), Gf.Vec3f(1.0, 0.0, 0.0), 1e-5)) async def test_urdf_mtl_stl(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_mtl_stl.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) mesh = stage.GetPrimAtPath("/test_mtl_stl/cube/visuals") self.assertTrue(UsdShade.MaterialBindingAPI(mesh) is not None) mat, rel = UsdShade.MaterialBindingAPI(mesh).ComputeBoundMaterial() shader = UsdShade.Shader(stage.GetPrimAtPath(mat.GetPath().pathString + "/Shader")) print(shader) self.assertTrue(Gf.IsClose(shader.GetInput("diffuse_color_constant").Get(), Gf.Vec3f(0.8, 0.0, 0), 1e-5)) async def test_urdf_carter(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/robots/carter/urdf/carter.urdf") status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.merge_fixed_joints = False status, path = omni.kit.commands.execute( "URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config ) self.assertTrue(path, "/carter") # TODO add checks here async def test_urdf_franka(self): urdf_path = os.path.abspath( self._extension_path + "/data/urdf/robots/franka_description/robots/panda_arm_hand.urdf" ) status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) # TODO add checks here' async def test_urdf_ur10(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/robots/ur10/urdf/ur10.urdf") status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) # TODO add checks here' async def test_urdf_kaya(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/robots/kaya/urdf/kaya.urdf") status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.merge_fixed_joints = False omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) # TODO add checks here async def test_missing(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_missing.urdf") status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) # This sample corresponds to the example in the docs, keep this and the version in the docs in sync async def test_doc_sample(self): import omni.kit.commands from pxr import Gf, Sdf, UsdLux, UsdPhysics # setting up import configuration: status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.merge_fixed_joints = False import_config.convex_decomp = False import_config.import_inertia_tensor = True import_config.fix_base = False # Get path to extension data: ext_manager = omni.kit.app.get_app().get_extension_manager() ext_id = ext_manager.get_enabled_extension_id("omni.importer.urdf") extension_path = ext_manager.get_extension_path(ext_id) # import URDF omni.kit.commands.execute( "URDFParseAndImportFile", urdf_path=extension_path + "/data/urdf/robots/carter/urdf/carter.urdf", import_config=import_config, ) # get stage handle stage = omni.usd.get_context().get_stage() # enable physics scene = UsdPhysics.Scene.Define(stage, Sdf.Path("/physicsScene")) # set gravity scene.CreateGravityDirectionAttr().Set(Gf.Vec3f(0.0, 0.0, -1.0)) scene.CreateGravityMagnitudeAttr().Set(9.81) # add ground plane PhysicsSchemaTools.addGroundPlane(stage, "/World/groundPlane", "Z", 1500, Gf.Vec3f(0, 0, -50), Gf.Vec3f(0.5)) # add lighting distantLight = UsdLux.DistantLight.Define(stage, Sdf.Path("/DistantLight")) distantLight.CreateIntensityAttr(500) #### #### Next Docs section #### # get handle to the Drive API for both wheels left_wheel_drive = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/carter/chassis_link/left_wheel"), "angular") right_wheel_drive = UsdPhysics.DriveAPI.Get(stage.GetPrimAtPath("/carter/chassis_link/right_wheel"), "angular") # Set the velocity drive target in degrees/second left_wheel_drive.GetTargetVelocityAttr().Set(150) right_wheel_drive.GetTargetVelocityAttr().Set(150) # Set the drive damping, which controls the strength of the velocity drive left_wheel_drive.GetDampingAttr().Set(15000) right_wheel_drive.GetDampingAttr().Set(15000) # Set the drive stiffness, which controls the strength of the position drive # In this case because we want to do velocity control this should be set to zero left_wheel_drive.GetStiffnessAttr().Set(0) right_wheel_drive.GetStiffnessAttr().Set(0) # Make sure that a urdf with more than 63 links imports async def test_64(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_large.urdf") status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) stage = omni.usd.get_context().get_stage() prim = stage.GetPrimAtPath("/test_large") self.assertTrue(prim) # basic urdf test: joints and links are imported correctly async def test_urdf_floating(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_floating.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.import_inertia_tensor = True omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) await omni.kit.app.get_app().next_update_async() prim = stage.GetPrimAtPath("/test_floating") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) # make sure the joints exist root_joint = stage.GetPrimAtPath("/test_floating/root_joint") self.assertNotEqual(root_joint.GetPath(), Sdf.Path.emptyPath) link_1 = stage.GetPrimAtPath("/test_floating/link_1") self.assertNotEqual(link_1.GetPath(), Sdf.Path.emptyPath) link_1_trans = np.array(omni.usd.get_world_transform_matrix(link_1).ExtractTranslation()) self.assertAlmostEqual(np.linalg.norm(link_1_trans - np.array([0, 0, 0.45])), 0, delta=0.03) floating_link = stage.GetPrimAtPath("/test_floating/floating_link") self.assertNotEqual(floating_link.GetPath(), Sdf.Path.emptyPath) floating_link_trans = np.array(omni.usd.get_world_transform_matrix(floating_link).ExtractTranslation()) self.assertAlmostEqual(np.linalg.norm(floating_link_trans - np.array([0, 0, 1.450])), 0, delta=0.03) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() pass async def test_urdf_scale(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_basic.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.distance_scale = 1.0 omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) await omni.kit.app.get_app().next_update_async() # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() self.assertAlmostEqual(UsdGeom.GetStageMetersPerUnit(stage), 1.0) pass async def test_urdf_drive_none(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_basic.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") from omni.importer.urdf._urdf import UrdfJointTargetType import_config.default_drive_type = UrdfJointTargetType.JOINT_DRIVE_NONE omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) await omni.kit.app.get_app().next_update_async() self.assertFalse(stage.GetPrimAtPath("/test_basic/root_joint").HasAPI(UsdPhysics.DriveAPI)) self.assertTrue(stage.GetPrimAtPath("/test_basic/link_1/elbow_joint").HasAPI(UsdPhysics.DriveAPI)) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() pass async def test_urdf_usd(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_usd.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") from omni.importer.urdf._urdf import UrdfJointTargetType import_config.default_drive_type = UrdfJointTargetType.JOINT_DRIVE_NONE omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) await omni.kit.app.get_app().next_update_async() self.assertNotEqual(stage.GetPrimAtPath("/test_usd/cube/visuals/mesh_0/Cylinder"), Sdf.Path.emptyPath) self.assertNotEqual(stage.GetPrimAtPath("/test_usd/cube/visuals/mesh_1/Torus"), Sdf.Path.emptyPath) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() pass # test negative joint limits async def test_urdf_limits(self): urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_limits.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.import_inertia_tensor = True omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) await omni.kit.app.get_app().next_update_async() # ensure the import completed. prim = stage.GetPrimAtPath("/test_limits") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) # ensure the joint limits are set on the elbow elbowJoint = stage.GetPrimAtPath("/test_limits/link_1/elbow_joint") self.assertNotEqual(elbowJoint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(elbowJoint.GetTypeName(), "PhysicsRevoluteJoint") self.assertTrue(elbowJoint.HasAPI(UsdPhysics.DriveAPI)) # ensure the joint limits are set on the wrist wristJoint = stage.GetPrimAtPath("/test_limits/link_2/wrist_joint") self.assertNotEqual(wristJoint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(wristJoint.GetTypeName(), "PhysicsRevoluteJoint") self.assertTrue(wristJoint.HasAPI(UsdPhysics.DriveAPI)) # ensure the joint limits are set on the finger1 finger1Joint = stage.GetPrimAtPath("/test_limits/palm_link/finger_1_joint") self.assertNotEqual(finger1Joint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(finger1Joint.GetTypeName(), "PhysicsPrismaticJoint") self.assertTrue(finger1Joint.HasAPI(UsdPhysics.DriveAPI)) # ensure the joint limits are set on the finger2 finger2Joint = stage.GetPrimAtPath("/test_limits/palm_link/finger_2_joint") self.assertNotEqual(finger2Joint.GetPath(), Sdf.Path.emptyPath) self.assertEqual(finger2Joint.GetTypeName(), "PhysicsPrismaticJoint") self.assertTrue(finger2Joint.HasAPI(UsdPhysics.DriveAPI)) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(1.0) # nothing crashes self._timeline.stop() pass # test collision from visuals async def test_collision_from_visuals(self): # import a urdf file without collision urdf_path = os.path.abspath(self._extension_path + "/data/urdf/tests/test_collision_from_visuals.urdf") stage = omni.usd.get_context().get_stage() status, import_config = omni.kit.commands.execute("URDFCreateImportConfig") import_config.set_collision_from_visuals(True) omni.kit.commands.execute("URDFParseAndImportFile", urdf_path=urdf_path, import_config=import_config) await omni.kit.app.get_app().next_update_async() # ensure the import completed. prim = stage.GetPrimAtPath("/test_collision_from_visuals") self.assertNotEqual(prim.GetPath(), Sdf.Path.emptyPath) # ensure the base_link collision prim exists and has the collision API applied. base_link = stage.GetPrimAtPath("/test_collision_from_visuals/base_link/collisions") self.assertNotEqual(base_link.GetPath(), Sdf.Path.emptyPath) self.assertTrue(base_link.GetAttribute("physics:collisionEnabled").Get()) # ensure the link_1 collision prim exists and has the collision API applied. link_1 = stage.GetPrimAtPath("/test_collision_from_visuals/link_1/collisions") self.assertNotEqual(link_1.GetPath(), Sdf.Path.emptyPath) self.assertTrue(link_1.GetAttribute("physics:collisionEnabled").Get()) # ensure the link_2 collision prim exists and has the collision API applied. link_2 = stage.GetPrimAtPath("/test_collision_from_visuals/link_2/collisions") self.assertNotEqual(link_2.GetPath(), Sdf.Path.emptyPath) self.assertTrue(link_2.GetAttribute("physics:collisionEnabled").Get()) # ensure the palm_link collision prim exists and has the collision API applied. palm_link = stage.GetPrimAtPath("/test_collision_from_visuals/palm_link/collisions") self.assertNotEqual(palm_link.GetPath(), Sdf.Path.emptyPath) self.assertTrue(palm_link.GetAttribute("physics:collisionEnabled").Get()) # ensure the finger_link_1 collision prim exists and has the collision API applied. finger_link_1 = stage.GetPrimAtPath("/test_collision_from_visuals/finger_link_1/collisions") self.assertNotEqual(finger_link_1.GetPath(), Sdf.Path.emptyPath) self.assertTrue(finger_link_1.GetAttribute("physics:collisionEnabled").Get()) # ensure the finger_link_2 collision prim exists and has the collision API applied. finger_link_2 = stage.GetPrimAtPath("/test_collision_from_visuals/finger_link_2/collisions") self.assertNotEqual(finger_link_2.GetPath(), Sdf.Path.emptyPath) self.assertTrue(finger_link_2.GetAttribute("physics:collisionEnabled").Get()) # Start Simulation and wait self._timeline.play() await omni.kit.app.get_app().next_update_async() await asyncio.sleep(2.0) # nothing crashes self._timeline.stop() pass

Python

NVIDIA-Omniverse/kit-extension-sample-defectsgen/exts/omni.example.defects/omni/example/defects/rep_widgets.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.ui as ui from .widgets import MinMaxWidget, CustomDirectory, PathWidget from .utils import * from pxr import Sdf from pathlib import Path import omni.kit.notification_manager as nm TEXTURE_DIR = Path(__file__).parent / "data" SCRATCHES_DIR = TEXTURE_DIR / "scratches" # Parameter Objects class DefectParameters: def __init__(self) -> None: self.semantic_label = ui.SimpleStringModel("defect") self.count = ui.SimpleIntModel(1) self._build_semantic_label() self.defect_text = CustomDirectory("Defect Texture Folder", default_dir=str(SCRATCHES_DIR.as_posix()), tooltip="A folder location containing a single or set of textures (.png)", file_types=[("*.png", "PNG"), ("*", "All Files")]) self.dim_w = MinMaxWidget("Defect Dimensions Width", min_value=0.1, tooltip="Defining the Minimum and Maximum Width of the Defect") self.dim_h = MinMaxWidget("Defect Dimensions Length", min_value=0.1, tooltip="Defining the Minimum and Maximum Length of the Defect") self.rot = MinMaxWidget("Defect Rotation", tooltip="Defining the Minimum and Maximum Rotation of the Defect") def _build_semantic_label(self): with ui.HStack(height=0, tooltip="The label that will be associated with the defect"): ui.Label("Defect Semantic") ui.StringField(model=self.semantic_label) def destroy(self): self.semantic_label = None self.defect_text.destroy() self.defect_text = None self.dim_w.destroy() self.dim_w = None self.dim_h.destroy() self.dim_h = None self.rot.destroy() self.rot = None class ObjectParameters(): def __init__(self) -> None: self.target_prim = PathWidget("Target Prim") def apply_primvars(prim): # Apply prim vars prim.CreateAttribute('primvars:d1_forward_vector', Sdf.ValueTypeNames.Float3, custom=True).Set((0,0,0)) prim.CreateAttribute('primvars:d1_right_vector', Sdf.ValueTypeNames.Float3, custom=True).Set((0,0,0)) prim.CreateAttribute('primvars:d1_up_vector', Sdf.ValueTypeNames.Float3, custom=True).Set((0,0,0)) prim.CreateAttribute('primvars:d1_position', Sdf.ValueTypeNames.Float3, custom=True).Set((0,0,0)) prim.CreateAttribute('primvars:v3_scale', Sdf.ValueTypeNames.Float3, custom=True).Set((0,0,0)) nm.post_notification(f"Applied Primvars to: {prim.GetPath()}", hide_after_timeout=True, duration=5, status=nm.NotificationStatus.INFO) def apply(): # Check Paths if not check_path(self.target_prim.path_value): return # Check if prim is valid prim = is_valid_prim(self.target_prim.path_value) if prim is None: return apply_primvars(prim) ui.Button("Apply", style={"padding": 5}, clicked_fn=lambda: apply(), tooltip="Apply Primvars and Material to selected Prim." ) def destroy(self): self.target_prim.destroy() self.target_prim = None class MaterialParameters(): def __init__(self) -> None: pass

Python

NVIDIA-Omniverse/kit-extension-sample-defectsgen/exts/omni.example.defects/omni/example/defects/widgets.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.ui as ui from omni.kit.window.file_importer import get_file_importer from typing import List import carb import omni.usd class CustomDirectory: def __init__(self, label: str, tooltip: str = "", default_dir: str = "", file_types: List[str] = None) -> None: self._label_text = label self._tooltip = tooltip self._file_types = file_types self._dir = ui.SimpleStringModel(default_dir) self._build_directory() @property def directory(self) -> str: """ Selected Directory name from file importer :type: str """ return self._dir.get_value_as_string() def _build_directory(self): with ui.HStack(height=0, tooltip=self._tooltip): ui.Label(self._label_text) ui.StringField(model=self._dir) ui.Button("Open", width=0, style={"padding": 5}, clicked_fn=self._pick_directory) def _pick_directory(self): file_importer = get_file_importer() if not file_importer: carb.log_warning("Unable to get file importer") file_importer.show_window(title="Select Folder", import_button_label="Import Directory", import_handler=self.import_handler, file_extension_types=self._file_types ) def import_handler(self, filename: str, dirname: str, selections: List[str] = []): self._dir.set_value(dirname) def destroy(self): self._dir = None class MinMaxWidget: def __init__(self, label: str, min_value: float = 0, max_value: float = 1, tooltip: str = "") -> None: self._min_model = ui.SimpleFloatModel(min_value) self._max_model = ui.SimpleFloatModel(max_value) self._label_text = label self._tooltip = tooltip self._build_min_max() @property def min_value(self) -> float: """ Min Value of the UI :type: int """ return self._min_model.get_value_as_float() @property def max_value(self) -> float: """ Max Value of the UI :type: int """ return self._max_model.get_value_as_float() def _build_min_max(self): with ui.HStack(height=0, tooltip=self._tooltip): ui.Label(self._label_text) with ui.HStack(): ui.Label("Min", width=0) ui.FloatDrag(model=self._min_model) ui.Label("Max", width=0) ui.FloatDrag(model=self._max_model) def destroy(self): self._max_model = None self._min_model = None class PathWidget: def __init__(self, label: str, button_label: str = "Copy", read_only: bool = False, tooltip: str = "") -> None: self._label_text = label self._tooltip = tooltip self._button_label = button_label self._read_only = read_only self._path_model = ui.SimpleStringModel() self._top_stack = ui.HStack(height=0, tooltip=self._tooltip) self._button = None self._build() @property def path_value(self) -> str: """ Path of the Prim in the scene :type: str """ return self._path_model.get_value_as_string() @path_value.setter def path_value(self, value) -> None: """ Sets the path value :type: str """ self._path_model.set_value(value) def _build(self): def copy(): ctx = omni.usd.get_context() selection = ctx.get_selection().get_selected_prim_paths() if len(selection) > 0: self._path_model.set_value(str(selection[0])) with self._top_stack: ui.Label(self._label_text) ui.StringField(model=self._path_model, read_only=self._read_only) self._button = ui.Button(self._button_label, width=0, style={"padding": 5}, clicked_fn=lambda: copy(), tooltip="Copies the Current Selected Path in the Stage") def destroy(self): self._path_model = None

Python

NVIDIA-Omniverse/kit-extension-sample-defectsgen/exts/omni.example.defects/omni/example/defects/extension.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.ext from .window import DefectsWindow class DefectsGenerator(omni.ext.IExt): WINDOW_NAME = "Defects Sample Extension" MENU_PATH = f"Window/{WINDOW_NAME}" def __init__(self) -> None: super().__init__() self._window = None def on_startup(self, ext_id): self._menu = omni.kit.ui.get_editor_menu().add_item( DefectsGenerator.MENU_PATH, self.show_window, toggle=True, value=True ) self.show_window(None, True) def on_shutdown(self): if self._menu: omni.kit.ui.get_editor_menu().remove_item(DefectsGenerator.MENU_PATH) self._menu if self._window: self._window.destroy() self._window = None def _set_menu(self, value): omni.kit.ui.get_editor_menu().set_value(DefectsGenerator.MENU_PATH, value) def _visibility_changed_fn(self, visible): self._set_menu(visible) if not visible: self._window = None def show_window(self, menu, value): self._set_menu(value) if value: self._set_menu(True) self._window = DefectsWindow(DefectsGenerator.WINDOW_NAME, width=450, height=700) self._window.set_visibility_changed_fn(self._visibility_changed_fn) elif self._window: self._window.visible = False

Python

NVIDIA-Omniverse/kit-extension-sample-defectsgen/exts/omni.example.defects/omni/example/defects/utils.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.usd import carb import omni.kit.commands import os def get_current_stage(): context = omni.usd.get_context() stage = context.get_stage() return stage def check_path(path: str) -> bool: if not path: carb.log_error("No path was given") return False return True def is_valid_prim(path: str): prim = get_prim(path) if not prim.IsValid(): carb.log_warn(f"No valid prim at path given: {path}") return None return prim def delete_prim(path: str): omni.kit.commands.execute('DeletePrims', paths=[path], destructive=False) def get_prim_attr(prim_path: str, attr_name: str): prim = get_prim(prim_path) return prim.GetAttribute(attr_name).Get() def get_textures(dir_path, png_type=".png"): textures = [] dir_path += "/" for file in os.listdir(dir_path): if file.endswith(png_type): textures.append(dir_path + file) return textures def get_prim(prim_path: str): stage = get_current_stage() prim = stage.GetPrimAtPath(prim_path) return prim

Python

NVIDIA-Omniverse/kit-extension-sample-defectsgen/exts/omni.example.defects/omni/example/defects/window.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import carb import omni.ui as ui from omni.ui import DockPreference from .style import * from .widgets import CustomDirectory from .replicator_defect import create_defect_layer, rep_preview, does_defect_layer_exist, rep_run, get_defect_layer from .rep_widgets import DefectParameters, ObjectParameters from .utils import * from pathlib import Path class DefectsWindow(ui.Window): def __init__(self, title: str, dockPreference: DockPreference = DockPreference.DISABLED, **kwargs) -> None: super().__init__(title, dockPreference, **kwargs) # Models self.frames = ui.SimpleIntModel(1, min=1) self.rt_subframes = ui.SimpleIntModel(1, min=1) # Widgets self.defect_params = None self.object_params = None self.output_dir = None self.frame_change = None self.frame.set_build_fn(self._build_frame) def _build_collapse_base(self, label: str, collapsed: bool = False): v_stack = None with ui.CollapsableFrame(label, height=0, collapsed=collapsed): with ui.ZStack(): ui.Rectangle() v_stack = ui.VStack() return v_stack def _build_frame(self): with self.frame: with ui.ScrollingFrame(style=default_defect_main): with ui.VStack(style={"margin": 3}): self._build_object_param() self._build_defect_param() self._build_replicator_param() def _build_object_param(self): with self._build_collapse_base("Object Parameters"): self.object_params = ObjectParameters() def _build_defect_param(self): with self._build_collapse_base("Defect Parameters"): self.defect_params = DefectParameters() def _build_replicator_param(self): def preview_data(): if does_defect_layer_exist(): rep_preview() else: create_defect_layer(self.defect_params, self.object_params) self.rep_layer_button.text = "Recreate Replicator Graph" def remove_replicator_graph(): if get_defect_layer() is not None: layer, pos = get_defect_layer() omni.kit.commands.execute('RemoveSublayer', layer_identifier=layer.identifier, sublayer_position=pos) if is_valid_prim('/World/Looks/ProjectPBRMaterial'): delete_prim('/World/Looks/ProjectPBRMaterial') if is_valid_prim(self.object_params.target_prim.path_value + "/Projection"): delete_prim(self.object_params.target_prim.path_value + "/Projection") if is_valid_prim('/Replicator'): delete_prim('/Replicator') def run_replicator(): remove_replicator_graph() total_frames = self.frames.get_value_as_int() subframes = self.rt_subframes.get_value_as_int() if subframes <= 0: subframes = 0 if total_frames > 0: create_defect_layer(self.defect_params, self.object_params, total_frames, self.output_dir.directory, subframes, self._use_seg.as_bool, self._use_bb.as_bool) self.rep_layer_button.text = "Recreate Replicator Graph" rep_run() else: carb.log_error(f"Number of frames is {total_frames}. Input value needs to be greater than 0.") def create_replicator_graph(): remove_replicator_graph() create_defect_layer(self.defect_params, self.object_params) self.rep_layer_button.text = "Recreate Replicator Graph" def set_text(label, model): label.text = model.as_string with self._build_collapse_base("Replicator Parameters"): home_dir = Path.home() valid_out_dir = home_dir / "omni.replicator_out" self.output_dir = CustomDirectory("Output Directory", default_dir=str(valid_out_dir.as_posix()), tooltip="Directory to specify where the output files will be stored. Default is [DRIVE/Users/USER/omni.replicator_out]") with ui.HStack(height=0, tooltip="Check off which annotator you want to use; You can also use both"): ui.Label("Annotations: ", width=0) ui.Spacer() ui.Label("Segmentation", width=0) self._use_seg = ui.CheckBox().model ui.Label("Bounding Box", width=0) self._use_bb = ui.CheckBox().model ui.Spacer() with ui.HStack(height=0): ui.Label("Render Subframe Count: ", width=0, tooltip="Defines how many subframes of rendering occur before going to the next frame") ui.Spacer(width=ui.Fraction(0.25)) ui.IntField(model=self.rt_subframes) self.rep_layer_button = ui.Button("Create Replicator Layer", clicked_fn=lambda: create_replicator_graph(), tooltip="Creates/Recreates the Replicator Graph, based on the current Defect Parameters") with ui.HStack(height=0): ui.Button("Preview", width=0, clicked_fn=lambda: preview_data(), tooltip="Preview a Replicator Scene") ui.Label("or", width=0) ui.Button("Run for", width=0, clicked_fn=lambda: run_replicator(), tooltip="Run replicator for so many frames") with ui.ZStack(width=0): l = ui.Label("", style={"color": ui.color.transparent, "margin_width": 10}) self.frame_change = ui.StringField(model=self.frames) self.frame_change_cb = self.frame_change.model.add_value_changed_fn(lambda m, l=l: set_text(l, m)) ui.Label("frame(s)") def destroy(self) -> None: self.frames = None self.defect_semantic = None if self.frame_change is not None: self.frame_change.model.remove_value_changed_fn(self.frame_change_cb) if self.defect_params is not None: self.defect_params.destroy() self.defect_params = None if self.object_params is not None: self.object_params.destroy() self.object_params = None return super().destroy()

Python

NVIDIA-Omniverse/kit-extension-sample-defectsgen/exts/omni.example.defects/omni/example/defects/replicator_defect.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.replicator.core as rep import carb from .rep_widgets import DefectParameters, ObjectParameters from .utils import * camera_path = "/World/Camera" def rep_preview(): rep.orchestrator.preview() def rep_run(): rep.orchestrator.run() def does_defect_layer_exist() -> bool: stage = get_current_stage() for layer in stage.GetLayerStack(): if layer.GetDisplayName() == "Defect": return True return False def get_defect_layer(): stage = get_current_stage() pos = 0 for layer in stage.GetLayerStack(): if layer.GetDisplayName() == "Defect": return layer, pos pos = pos + 1 return None def create_randomizers(defect_params: DefectParameters, object_params: ObjectParameters): diffuse_textures = get_textures(defect_params.defect_text.directory, "_D.png") normal_textures = get_textures(defect_params.defect_text.directory, "_N.png") roughness_textures = get_textures(defect_params.defect_text.directory, "_R.png") def move_defect(): defects = rep.get.prims(semantics=[('class', defect_params.semantic_label.as_string + '_mesh')]) plane = rep.get.prim_at_path(object_params.target_prim.path_value) with defects: rep.randomizer.scatter_2d(plane) rep.modify.pose( rotation=rep.distribution.uniform( (defect_params.rot.min_value, 0, 90), (defect_params.rot.max_value, 0, 90) ), scale=rep.distribution.uniform( (1, defect_params.dim_h.min_value,defect_params.dim_w.min_value), (1, defect_params.dim_h.max_value, defect_params.dim_w.max_value) ) ) return defects.node def change_defect_image(): projections = rep.get.prims(semantics=[('class', defect_params.semantic_label.as_string + '_projectmat')]) with projections: rep.modify.projection_material( diffuse=rep.distribution.sequence(diffuse_textures), normal=rep.distribution.sequence(normal_textures), roughness=rep.distribution.sequence(roughness_textures)) return projections.node rep.randomizer.register(move_defect) rep.randomizer.register(change_defect_image) def create_camera(target_path): if is_valid_prim(camera_path) is None: camera = rep.create.camera(position=1000, look_at=rep.get.prim_at_path(target_path)) carb.log_info(f"Creating Camera: {camera}") else: camera = rep.get.prim_at_path(camera_path) return camera def create_defects(defect_params: DefectParameters, object_params: ObjectParameters): target_prim = rep.get.prims(path_pattern=object_params.target_prim.path_value) count = 1 if defect_params.count.as_int > 1: count = defect_params.count.as_int for i in range(count): cube = rep.create.cube(visible=False, semantics=[('class', defect_params.semantic_label.as_string + '_mesh')], position=0, scale=1, rotation=(0, 0, 90)) with target_prim: rep.create.projection_material(cube, [('class', defect_params.semantic_label.as_string + '_projectmat')]) def create_defect_layer(defect_params: DefectParameters, object_params: ObjectParameters, frames: int = 1, output_dir: str = "_defects", rt_subframes: int = 0, use_seg: bool = False, use_bb: bool = True): if len(defect_params.defect_text.directory) <= 0: carb.log_error("No directory selected") return with rep.new_layer("Defect"): create_defects(defect_params, object_params) create_randomizers(defect_params=defect_params, object_params=object_params) # Create / Get camera camera = create_camera(object_params.target_prim.path_value) # Add Default Light distance_light = rep.create.light(rotation=(315,0,0), intensity=3000, light_type="distant") render_product = rep.create.render_product(camera, (1024, 1024)) # Initialize and attach writer writer = rep.WriterRegistry.get("BasicWriter") writer.initialize(output_dir=output_dir, rgb=True, semantic_segmentation=use_seg, bounding_box_2d_tight=use_bb) # Attach render_product to the writer writer.attach([render_product]) # Setup randomization with rep.trigger.on_frame(num_frames=frames, rt_subframes=rt_subframes): rep.randomizer.move_defect() rep.randomizer.change_defect_image()

Python

NVIDIA-Omniverse/kit-extension-sample-apiconnect/exts/omni.example.apiconnect/omni/example/apiconnect/extension.py

# SPDX-License-Identifier: Apache-2.0 import asyncio import aiohttp import carb import omni.ext import omni.ui as ui class APIWindowExample(ui.Window): def __init__(self, title: str, **kwargs) -> None: """ Initialize the widget. Args: title : Title of the widget. This is used to display the window title on the GUI. """ super().__init__(title, **kwargs) self.frame.set_build_fn(self._build_fn) # async function to get the color palette from huemint.com and print it async def get_colors_from_api(self, color_widgets): """ Get colors from HueMint API and store them in color_widgets. Args: color_widgets : List of widgets to """ # Create the task for progress indication and change button text self.button.text = "Loading" task = asyncio.create_task(self.run_forever()) # Create a aiohttp session to make the request, building the url and the data to send # By default it will timeout after 5 minutes. # See more here: https://docs.aiohttp.org/en/latest/client_quickstart.html async with aiohttp.ClientSession() as session: url = "https://api.huemint.com/color" data = { "mode": "transformer", # transformer, diffusion or random "num_colors": "5", # max 12, min 2 "temperature": "1.2", # max 2.4, min 0 "num_results": "1", # max 50 for transformer, 5 for diffusion "adjacency": ["0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0", ], # nxn adjacency matrix as a flat array of strings "palette": ["-", "-", "-", "-", "-"], # locked colors as hex codes, or '-' if blank } # make the request try: async with session.post(url, json=data) as resp: # get the response as json result = await resp.json(content_type=None) # get the palette from the json palette = result["results"][0]["palette"] # apply the colors to the color widgets await self.apply_colors(palette, color_widgets) # Cancel the progress indication and return the button to the original text task.cancel() self.button.text = "Refresh" except Exception as e: carb.log_info(f"Caught Exception {e}") # Cancel the progress indication and return the button to the original text task.cancel() self.button.text = "Connection Timed Out \nClick to Retry" # apply the colors fetched from the api to the color widgets async def apply_colors(self, palette, color_widgets): """ Apply the colors to the ColorWidget. This is a helper method to allow us to get the color values from the API and set them in the color widgets Args: palette : The palette that we want to apply color_widgets : The list of color widgets """ colors = [palette[i] for i in range(5)] index = 0 # This will fetch the RGB colors from the color widgets and set them to the color of the color widget. for color_widget in color_widgets: await omni.kit.app.get_app().next_update_async() # we get the individual RGB colors from ColorWidget model color_model = color_widget.model children = color_model.get_item_children() hex_to_float = self.hextofloats(colors[index]) # we set the color of the color widget to the color fetched from the api color_model.get_item_value_model(children[0]).set_value(hex_to_float[0]) color_model.get_item_value_model(children[1]).set_value(hex_to_float[1]) color_model.get_item_value_model(children[2]).set_value(hex_to_float[2]) index = index + 1 async def run_forever(self): """ Run the loop until we get a response from omni. """ count = 0 dot_count = 0 # Update the button text. while True: # Reset the button text to Loading if count % 10 == 0: # Reset the text for the button # Add a dot after Loading. if dot_count == 3: self.button.text = "Loading" dot_count = 0 # Add a dot after Loading else: self.button.text += "." dot_count += 1 count += 1 await omni.kit.app.get_app().next_update_async() # hex to float conversion for transforming hex color codes to float values def hextofloats(self, h): """ Convert hex values to floating point numbers. This is useful for color conversion to a 3 or 5 digit hex value Args: h : RGB string in the format 0xRRGGBB Returns: float tuple of ( r g b ) where r g b are floats between 0 and 1 and b """ # Convert hex rgb string in an RGB tuple (float, float, float) return tuple(int(h[i : i + 2], 16) / 255.0 for i in (1, 3, 5)) # skip '#' def _build_fn(self): """ Build the function to call the api when the app starts. """ with self.frame: with ui.VStack(alignment=ui.Alignment.CENTER): # Get the run loop run_loop = asyncio.get_event_loop() ui.Label("Click the button to get a new color palette", height=30, alignment=ui.Alignment.CENTER) with ui.HStack(height=100): color_widgets = [ui.ColorWidget(1, 1, 1) for i in range(5)] def on_click(): """ Get colors from API and run task in run_loop. This is called when user clicks the button """ run_loop.create_task(self.get_colors_from_api(color_widgets)) # create a button to trigger the api call self.button = ui.Button("Refresh", clicked_fn=on_click) # we execute the api call once on startup run_loop.create_task(self.get_colors_from_api(color_widgets)) # Any class derived from `omni.ext.IExt` in top level module (defined in `python.modules` of `extension.toml`) will be # instantiated when extension gets enabled and `on_startup(ext_id)` will be called. Later when extension gets disabled # on_shutdown() is called. class MyExtension(omni.ext.IExt): # ext_id is current extension id. It can be used with extension manager to query additional information, like where # this extension is located on filesystem. def on_startup(self, ext_id): """ Called when the extension is started. Args: ext_id - id of the extension """ print("[omni.example.apiconnect] MyExtension startup") # create a new window self._window = APIWindowExample("API Connect Demo - HueMint", width=260, height=270) def on_shutdown(self): """ Called when the extension is shut down. Destroys the window if it exists and sets it to None """ print("[omni.example.apiconnect] MyExtension shutdown") # Destroys the window and releases the reference to the window. if self._window: self._window.destroy() self._window = None

Python

NVIDIA-Omniverse/kit-extension-sample-apiconnect/exts/omni.example.apiconnect/omni/example/apiconnect/__init__.py

# SPDX-License-Identifier: Apache-2.0 from .extension import *

Python

NVIDIA-Omniverse/kit-extension-sample-reticle/exts/omni.example.reticle/omni/example/reticle/__init__.py

from .extension import ExampleViewportReticleExtension

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_gradient_window/omni/example/ui_gradient_window/style.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["main_window_style"] from omni.ui import color as cl from omni.ui import constant as fl from omni.ui import url import omni.kit.app import omni.ui as ui import pathlib EXTENSION_FOLDER_PATH = pathlib.Path( omni.kit.app.get_app().get_extension_manager().get_extension_path_by_module(__name__) ) # Pre-defined constants. It's possible to change them runtime. fl_attr_hspacing = 10 fl_attr_spacing = 1 fl_group_spacing = 5 cl_attribute_dark = cl("#202324") cl_attribute_red = cl("#ac6060") cl_attribute_green = cl("#60ab7c") cl_attribute_blue = cl("#35889e") cl_line = cl("#404040") cl_text_blue = cl("#5eb3ff") cl_text_gray = cl("#707070") cl_text = cl("#a1a1a1") cl_text_hovered = cl("#ffffff") cl_field_text = cl("#5f5f5f") cl_widget_background = cl("#1f2123") cl_attribute_default = cl("#505050") cl_attribute_changed = cl("#55a5e2") cl_slider = cl("#383b3e") cl_combobox_background = cl("#252525") cl_main_background = cl("#2a2b2c") cls_temperature_gradient = [cl("#fe0a00"), cl("#f4f467"), cl("#a8b9ea"), cl("#2c4fac"), cl("#274483"), cl("#1f334e")] cls_color_gradient = [cl("#fa0405"), cl("#95668C"), cl("#4b53B4"), cl("#33C287"), cl("#9fE521"), cl("#ff0200")] cls_tint_gradient = [cl("#1D1D92"), cl("#7E7EC9"), cl("#FFFFFF")] cls_grey_gradient = [cl("#020202"), cl("#525252"), cl("#FFFFFF")] cls_button_gradient = [cl("#232323"), cl("#656565")] # The main style dict main_window_style = { "Button::add": {"background_color": cl_widget_background}, "Field::add": { "font_size": 14, "color": cl_text}, "Field::search": { "font_size": 16, "color": cl_field_text}, "Field::path": { "font_size": 14, "color": cl_field_text}, "ScrollingFrame::main_frame": {"background_color": cl_main_background}, # for CollapsableFrame "CollapsableFrame::group": { "margin_height": fl_group_spacing, "background_color": 0x0, "secondary_color": 0x0, }, "CollapsableFrame::group:hovered": { "margin_height": fl_group_spacing, "background_color": 0x0, "secondary_color": 0x0, }, # for Secondary CollapsableFrame "Circle::group_circle": { "background_color": cl_line, }, "Line::group_line": {"color": cl_line}, # all the labels "Label::collapsable_name": { "alignment": ui.Alignment.LEFT_CENTER, "color": cl_text }, "Label::attribute_bool": { "alignment": ui.Alignment.LEFT_BOTTOM, "margin_height": fl_attr_spacing, "margin_width": fl_attr_hspacing, "color": cl_text }, "Label::attribute_name": { "alignment": ui.Alignment.RIGHT_CENTER, "margin_height": fl_attr_spacing, "margin_width": fl_attr_hspacing, "color": cl_text }, "Label::attribute_name:hovered": {"color": cl_text_hovered}, "Label::header_attribute_name": { "alignment": ui.Alignment.LEFT_CENTER, "color": cl_text }, "Label::details": { "alignment": ui.Alignment.LEFT_CENTER, "color": cl_text_blue, "font_size": 19, }, "Label::layers": { "alignment": ui.Alignment.LEFT_CENTER, "color": cl_text_gray, "font_size": 19, }, "Label::attribute_r": { "alignment": ui.Alignment.LEFT_CENTER, "color": cl_attribute_red }, "Label::attribute_g": { "alignment": ui.Alignment.LEFT_CENTER, "color": cl_attribute_green }, "Label::attribute_b": { "alignment": ui.Alignment.LEFT_CENTER, "color": cl_attribute_blue }, # for Gradient Float Slider "Slider::float_slider":{ "background_color": cl_widget_background, "secondary_color": cl_slider, "border_radius": 3, "corner_flag": ui.CornerFlag.ALL, "draw_mode": ui.SliderDrawMode.FILLED, }, # for color slider "Circle::slider_handle":{"background_color": 0x0, "border_width": 2, "border_color": cl_combobox_background}, # for Value Changed Widget "Rectangle::attribute_changed": {"background_color":cl_attribute_changed, "border_radius": 2}, "Rectangle::attribute_default": {"background_color":cl_attribute_default, "border_radius": 1}, # all the images "Image::pin": {"image_url": f"{EXTENSION_FOLDER_PATH}/icons/Pin.svg"}, "Image::expansion": {"image_url": f"{EXTENSION_FOLDER_PATH}/icons/Details_options.svg"}, "Image::transform": {"image_url": f"{EXTENSION_FOLDER_PATH}/icons/offset_dark.svg"}, "Image::link": {"image_url": f"{EXTENSION_FOLDER_PATH}/icons/link_active_dark.svg"}, "Image::on_off": {"image_url": f"{EXTENSION_FOLDER_PATH}/icons/on_off.svg"}, "Image::header_frame": {"image_url": f"{EXTENSION_FOLDER_PATH}/icons/head.png"}, "Image::checked": {"image_url": f"{EXTENSION_FOLDER_PATH}/icons/checked.svg"}, "Image::unchecked": {"image_url": f"{EXTENSION_FOLDER_PATH}/icons/unchecked.svg"}, "Image::separator":{"image_url": f"{EXTENSION_FOLDER_PATH}/icons/separator.svg"}, "Image::collapsable_opened": {"color": cl_text, "image_url": f"{EXTENSION_FOLDER_PATH}/icons/closed.svg"}, "Image::collapsable_closed": {"color": cl_text, "image_url": f"{EXTENSION_FOLDER_PATH}/icons/open.svg"}, "Image::combobox": {"image_url": f"{EXTENSION_FOLDER_PATH}/icons/combobox_bg.svg"}, # for Gradient Image "ImageWithProvider::gradient_slider":{"border_radius": 4, "corner_flag": ui.CornerFlag.ALL}, "ImageWithProvider::button_background_gradient": {"border_radius": 3, "corner_flag": ui.CornerFlag.ALL}, # for Customized ComboBox "ComboBox::dropdown_menu":{ "color": cl_text, # label color "background_color": cl_combobox_background, "secondary_color": 0x0, # button background color }, } def hex_to_color(hex: int) -> tuple: # convert Value from int red = hex & 255 green = (hex >> 8) & 255 blue = (hex >> 16) & 255 alpha = (hex >> 24) & 255 rgba_values = [red, green, blue, alpha] return rgba_values def _interpolate_color(hex_min: int, hex_max: int, intep): max_color = hex_to_color(hex_max) min_color = hex_to_color(hex_min) color = [int((max - min) * intep) + min for max, min in zip(max_color, min_color)] return (color[3] << 8 * 3) + (color[2] << 8 * 2) + (color[1] << 8 * 1) + color[0] def get_gradient_color(value, max, colors): step_size = len(colors) - 1 step = 1.0/float(step_size) percentage = value / float(max) idx = (int) (percentage / step) if idx == step_size: color = colors[-1] else: color = _interpolate_color(colors[idx], colors[idx+1], percentage) return color def generate_byte_data(colors): data = [] for color in colors: data += hex_to_color(color) _byte_provider = ui.ByteImageProvider() _byte_provider.set_bytes_data(data, [len(colors), 1]) return _byte_provider def build_gradient_image(colors, height, style_name): byte_provider = generate_byte_data(colors) ui.ImageWithProvider(byte_provider,fill_policy=omni.ui.IwpFillPolicy.IWP_STRETCH, height=height, name=style_name) return byte_provider

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_gradient_window/omni/example/ui_gradient_window/extension.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["ExampleWindowExtension"] from .window import PropertyWindowExample from functools import partial import asyncio import omni.ext import omni.kit.ui import omni.ui as ui class ExampleWindowExtension(omni.ext.IExt): """The entry point for Gradient Style Window Example""" WINDOW_NAME = "Gradient Style Window Example" MENU_PATH = f"Window/{WINDOW_NAME}" def on_startup(self): # The ability to show up the window if the system requires it. We use it # in QuickLayout. ui.Workspace.set_show_window_fn(ExampleWindowExtension.WINDOW_NAME, partial(self.show_window, None)) # Put the new menu editor_menu = omni.kit.ui.get_editor_menu() if editor_menu: self._menu = editor_menu.add_item( ExampleWindowExtension.MENU_PATH, self.show_window, toggle=True, value=True ) # Show the window. It will call `self.show_window` ui.Workspace.show_window(ExampleWindowExtension.WINDOW_NAME) def on_shutdown(self): self._menu = None if self._window: self._window.destroy() self._window = None # Deregister the function that shows the window from omni.ui ui.Workspace.set_show_window_fn(ExampleWindowExtension.WINDOW_NAME, None) def _set_menu(self, value): """Set the menu to create this window on and off""" editor_menu = omni.kit.ui.get_editor_menu() if editor_menu: editor_menu.set_value(ExampleWindowExtension.MENU_PATH, value) async def _destroy_window_async(self): # wait one frame, this is due to the one frame defer # in Window::_moveToMainOSWindow() await omni.kit.app.get_app().next_update_async() if self._window: self._window.destroy() self._window = None def _visiblity_changed_fn(self, visible): # Called when the user pressed "X" self._set_menu(visible) if not visible: # Destroy the window, since we are creating new window # in show_window asyncio.ensure_future(self._destroy_window_async()) def show_window(self, menu, value): if value: self._window = PropertyWindowExample(ExampleWindowExtension.WINDOW_NAME, width=450, height=900) self._window.set_visibility_changed_fn(self._visiblity_changed_fn) elif self._window: self._window.visible = False

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_gradient_window/omni/example/ui_gradient_window/collapsable_widget.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["CustomCollsableFrame"] import omni.ui as ui def build_collapsable_header(collapsed, title): """Build a custom title of CollapsableFrame""" with ui.HStack(): ui.Spacer(width=10) ui.Label(title, name="collapsable_name") if collapsed: image_name = "collapsable_opened" else: image_name = "collapsable_closed" ui.Image(name=image_name, fill_policy=ui.FillPolicy.PRESERVE_ASPECT_FIT, width=16, height=16) class CustomCollsableFrame: """The compound widget for color input""" def __init__(self, frame_name, collapsed=False): with ui.ZStack(): self.collapsable_frame = ui.CollapsableFrame( frame_name, name="group", build_header_fn=build_collapsable_header, collapsed=collapsed) with ui.VStack(): ui.Spacer(height=29) with ui.HStack(): ui.Spacer(width=20) ui.Image(name="separator", fill_policy=ui.FillPolicy.STRETCH, height=15) ui.Spacer(width=20)

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_gradient_window/omni/example/ui_gradient_window/color_widget.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["ColorWidget"] from ctypes import Union from typing import List, Optional import omni.ui as ui from .style import build_gradient_image, cl_attribute_red, cl_attribute_green, cl_attribute_blue, cl_attribute_dark SPACING = 16 class ColorWidget: """The compound widget for color input""" def __init__(self, *args, model=None, **kwargs): self.__defaults: List[Union[float, int]] = args self.__model: Optional[ui.AbstractItemModel] = kwargs.pop("model", None) self.__multifield: Optional[ui.MultiFloatDragField] = None self.__colorpicker: Optional[ui.ColorWidget] = None self.__draw_colorpicker = kwargs.pop("draw_colorpicker", True) self.__frame = ui.Frame() with self.__frame: self._build_fn() def destroy(self): self.__model = None self.__multifield = None self.__colorpicker = None self.__frame = None def __getattr__(self, attr): """ Pretend it's self.__frame, so we have access to width/height and callbacks. """ return getattr(self.__root_frame, attr) @property def model(self) -> Optional[ui.AbstractItemModel]: """The widget's model""" if self.__multifield: return self.__multifield.model @model.setter def model(self, value: ui.AbstractItemModel): """The widget's model""" self.__multifield.model = value self.__colorpicker.model = value def _build_fn(self): def _on_value_changed(model, rect_changed, rect_default): if model.get_item_value_model().get_value_as_float() != 0: rect_changed.visible = False rect_default.visible = True else: rect_changed.visible = True rect_default.visible = False def _restore_default(model, rect_changed, rect_default): items = model.get_item_children() for id, item in enumerate(items): model.get_item_value_model(item).set_value(self.__defaults[id]) rect_changed.visible = False rect_default.visible = True with ui.HStack(spacing=SPACING): # The construction of multi field depends on what the user provided, # defaults or a model if self.__model: # the user provided a model self.__multifield = ui.MultiFloatDragField( min=0, max=1, model=self.__model, h_spacing=SPACING, name="attribute_color" ) model = self.__model else: # the user provided a list of default values with ui.ZStack(): with ui.HStack(): self.color_button_gradient_R = build_gradient_image([cl_attribute_dark, cl_attribute_red], 22, "button_background_gradient") ui.Spacer(width=9) with ui.VStack(width=6): ui.Spacer(height=8) ui.Circle(name="group_circle", width=4, height=4) self.color_button_gradient_G = build_gradient_image([cl_attribute_dark, cl_attribute_green], 22, "button_background_gradient") ui.Spacer(width=9) with ui.VStack(width=6): ui.Spacer(height=8) ui.Circle(name="group_circle", width=4, height=4) self.color_button_gradient_B = build_gradient_image([cl_attribute_dark, cl_attribute_blue], 22, "button_background_gradient") ui.Spacer(width=2) with ui.HStack(): with ui.VStack(): ui.Spacer(height=1) self.__multifield = ui.MultiFloatDragField( *self.__defaults, min=0, max=1, h_spacing=SPACING, name="attribute_color") ui.Spacer(width=3) with ui.HStack(spacing=22): labels = ["R", "G", "B"] if self.__draw_colorpicker else ["X", "Y", "Z"] ui.Label(labels[0], name="attribute_r") ui.Label(labels[1], name="attribute_g") ui.Label(labels[2], name="attribute_b") model = self.__multifield.model if self.__draw_colorpicker: self.__colorpicker = ui.ColorWidget(model, width=0) rect_changed, rect_default = self.__build_value_changed_widget() model.add_item_changed_fn(lambda model, i: _on_value_changed(model, rect_changed, rect_default)) rect_changed.set_mouse_pressed_fn(lambda x, y, b, m: _restore_default(model, rect_changed, rect_default)) def __build_value_changed_widget(self): with ui.VStack(width=0): ui.Spacer(height=3) rect_changed = ui.Rectangle(name="attribute_changed", width=15, height=15, visible= False) ui.Spacer(height=4) with ui.HStack(): ui.Spacer(width=3) rect_default = ui.Rectangle(name="attribute_default", width=5, height=5, visible= True) return rect_changed, rect_default

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_gradient_window/omni/example/ui_gradient_window/window.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved./icons/ # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["PropertyWindowExample"] from ast import With from ctypes import alignment import omni.kit import omni.ui as ui from .style import main_window_style, get_gradient_color, build_gradient_image from .style import cl_combobox_background, cls_temperature_gradient, cls_color_gradient, cls_tint_gradient, cls_grey_gradient, cls_button_gradient from .color_widget import ColorWidget from .collapsable_widget import CustomCollsableFrame, build_collapsable_header LABEL_WIDTH = 120 SPACING = 10 def _get_plus_glyph(): return omni.kit.ui.get_custom_glyph_code("${glyphs}/menu_context.svg") def _get_search_glyph(): return omni.kit.ui.get_custom_glyph_code("${glyphs}/menu_search.svg") class PropertyWindowExample(ui.Window): """The class that represents the window""" def __init__(self, title: str, delegate=None, **kwargs): self.__label_width = LABEL_WIDTH super().__init__(title, **kwargs) # Apply the style to all the widgets of this window self.frame.style = main_window_style # Set the function that is called to build widgets when the window is visible self.frame.set_build_fn(self._build_fn) def destroy(self): # It will destroy all the children super().destroy() @property def label_width(self): """The width of the attribute label""" return self.__label_width @label_width.setter def label_width(self, value): """The width of the attribute label""" self.__label_width = value self.frame.rebuild() def _build_transform(self): """Build the widgets of the "Calculations" group""" with ui.ZStack(): with ui.VStack(): ui.Spacer(height=5) with ui.HStack(): ui.Spacer() ui.Image(name="transform", fill_policy=ui.FillPolicy.PRESERVE_ASPECT_FIT, width=24, height=24) ui.Spacer(width=30) ui.Spacer() with CustomCollsableFrame("TRANSFORMS").collapsable_frame: with ui.VStack(height=0, spacing=SPACING): ui.Spacer(height=2) self._build_vector_widget("Position", 70) self._build_vector_widget("Rotation", 70) with ui.ZStack(): self._build_vector_widget("Scale", 85) with ui.HStack(): ui.Spacer(width=42) ui.Image(name="link", fill_policy=ui.FillPolicy.PRESERVE_ASPECT_FIT, width=20) def _build_path(self): CustomCollsableFrame("PATH", collapsed=True) def _build_light_properties(self): """Build the widgets of the "Parameters" group""" with CustomCollsableFrame("LIGHT PROPERTIES").collapsable_frame: with ui.VStack(height=0, spacing=SPACING): ui.Spacer(height=2) self._build_combobox("Type", ["Sphere Light", "Disk Light", "Rect Light"]) self.color_gradient_data, self.tint_gradient_data, self.grey_gradient_data = self._build_color_widget("Color") self._build_color_temperature() self.diffuse_button_data = self._build_gradient_float_slider("Diffuse Multiplier") self.exposture_button_data = self._build_gradient_float_slider("Exposture") self.intensity_button_data = self._build_gradient_float_slider("Intensity", default_value=3000, min=0, max=6000) self._build_checkbox("Normalize Power", False) self._build_combobox("Purpose", ["Default", "Customized"]) self.radius_button_data = self._build_gradient_float_slider("Radius") self._build_shaping() self.specular_button_data = self._build_gradient_float_slider("Specular Multiplier") self._build_checkbox("Treat As Point") def _build_line_dot(self, line_width, height): with ui.HStack(): ui.Spacer(width=10) with ui.VStack(width=line_width): ui.Spacer(height=height) ui.Line(name="group_line", alignment=ui.Alignment.TOP) with ui.VStack(width=6): ui.Spacer(height=height-2) ui.Circle(name="group_circle", width=6, height=6, alignment=ui.Alignment.BOTTOM) def _build_shaping(self): """Build the widgets of the "SHAPING" group""" with ui.ZStack(): with ui.HStack(): ui.Spacer(width=3) self._build_line_dot(10, 17) with ui.HStack(): ui.Spacer(width=13) with ui.VStack(): ui.Spacer(height=17) ui.Line(name="group_line", alignment=ui.Alignment.RIGHT, width=0) ui.Spacer(height=80) with ui.CollapsableFrame(" SHAPING", name="group", build_header_fn=build_collapsable_header): with ui.VStack(height=0, spacing=SPACING): self.angle_button_data = self._build_gradient_float_slider("Cone Angle") self.softness_button_data = self._build_gradient_float_slider("Cone Softness") self.focus_button_data = self._build_gradient_float_slider("Focus") self.focus_color_data, self.focus_tint_data, self.focus_grey_data = self._build_color_widget("Focus Tint") def _build_vector_widget(self, widget_name, space): with ui.HStack(): ui.Label(widget_name, name="attribute_name", width=0) ui.Spacer(width=space) # The custom compound widget ColorWidget(1.0, 1.0, 1.0, draw_colorpicker=False) ui.Spacer(width=10) def _build_color_temperature(self): with ui.ZStack(): with ui.HStack(): ui.Spacer(width=10) with ui.VStack(): ui.Spacer(height=8) ui.Line(name="group_line", alignment=ui.Alignment.RIGHT, width=0) with ui.VStack(height=0, spacing=SPACING): with ui.HStack(): self._build_line_dot(10, 8) ui.Label("Enable Color Temperature", name="attribute_name", width=0) ui.Spacer() ui.Image(name="on_off", fill_policy=ui.FillPolicy.PRESERVE_ASPECT_FIT, width=20) rect_changed, rect_default = self.__build_value_changed_widget() self.temperature_button_data = self._build_gradient_float_slider(" Color Temperature", default_value=6500.0) self.temperature_slider_data = self._build_slider_handle(cls_temperature_gradient) with ui.HStack(): ui.Spacer(width=10) ui.Line(name="group_line", alignment=ui.Alignment.TOP) def _build_color_widget(self, widget_name): with ui.ZStack(): with ui.HStack(): ui.Spacer(width=10) with ui.VStack(): ui.Spacer(height=8) ui.Line(name="group_line", alignment=ui.Alignment.RIGHT, width=0) with ui.VStack(height=0, spacing=SPACING): with ui.HStack(): self._build_line_dot(40, 9) ui.Label(widget_name, name="attribute_name", width=0) # The custom compound widget ColorWidget(0.25, 0.5, 0.75) ui.Spacer(width=10) color_data = self._build_slider_handle(cls_color_gradient) tint_data = self._build_slider_handle(cls_tint_gradient) grey_data = self._build_slider_handle(cls_grey_gradient) with ui.HStack(): ui.Spacer(width=10) ui.Line(name="group_line", alignment=ui.Alignment.TOP) return color_data, tint_data, grey_data def _build_slider_handle(self, colors): handle_Style = {"background_color": colors[0], "border_width": 2, "border_color": cl_combobox_background} def set_color(placer, handle, offset): # first clamp the value max = placer.computed_width - handle.computed_width if offset < 0: placer.offset_x = 0 elif offset > max: placer.offset_x = max color = get_gradient_color(placer.offset_x.value, max, colors) handle_Style.update({"background_color": color}) handle.style = handle_Style with ui.HStack(): ui.Spacer(width=18) with ui.ZStack(): with ui.VStack(): ui.Spacer(height=3) byte_provider = build_gradient_image(colors, 8, "gradient_slider") with ui.HStack(): handle_placer = ui.Placer(draggable=True, drag_axis=ui.Axis.X, offset_x=0) with handle_placer: handle = ui.Circle(width=15, height=15, style=handle_Style) handle_placer.set_offset_x_changed_fn(lambda offset: set_color(handle_placer, handle, offset.value)) ui.Spacer(width=22) return byte_provider def _build_fn(self): """ The method that is called to build all the UI once the window is visible. """ with ui.ScrollingFrame(name="main_frame"): with ui.VStack(height=0, spacing=SPACING): self._build_head() self._build_transform() self._build_path() self._build_light_properties() ui.Spacer(height=30) def _build_head(self): with ui.ZStack(): ui.Image(name="header_frame", height=150, fill_policy=ui.FillPolicy.STRETCH) with ui.HStack(): ui.Spacer(width=12) with ui.VStack(spacing=8): self._build_tabs() ui.Spacer(height=1) self._build_selection_widget() self._build_stage_path_widget() self._build_search_field() ui.Spacer(width=12) def _build_tabs(self): with ui.HStack(height=35): ui.Label("DETAILS", width=ui.Percent(17), name="details") with ui.ZStack(): ui.Image(name="combobox", fill_policy=ui.FillPolicy.STRETCH, height=35) with ui.HStack(): ui.Spacer(width=15) ui.Label("LAYERS | ", name="layers", width=0) ui.Label(f"{_get_plus_glyph()}", width=0) ui.Spacer() ui.Image(name="pin", width=20) def _build_selection_widget(self): with ui.HStack(height=20): add_button = ui.Button(f"{_get_plus_glyph()} Add", width=60, name="add") ui.Spacer(width=14) ui.StringField(name="add").model.set_value("(2 models selected)") ui.Spacer(width=8) ui.Image(name="expansion", width=20) def _build_stage_path_widget(self): with ui.HStack(height=20): ui.Spacer(width=3) ui.Label("Stage Path", name="header_attribute_name", width=70) ui.StringField(name="path").model.set_value("/World/environment/tree") def _build_search_field(self): with ui.HStack(): ui.Spacer(width=2) # would add name="search" style, but there is a bug to use glyph together with style # make sure the test passes for now ui.StringField(height=23).model.set_value(f"{_get_search_glyph()} Search") def _build_checkbox(self, label_name, default_value=True): def _restore_default(rect_changed, rect_default): image.name = "checked" if default_value else "unchecked" rect_changed.visible = False rect_default.visible = True def _on_value_changed(image, rect_changed, rect_default): image.name = "unchecked" if image.name == "checked" else "checked" if (default_value and image.name == "unchecked") or (not default_value and image.name == "checked"): rect_changed.visible = True rect_default.visible = False else: rect_changed.visible = False rect_default.visible = True with ui.HStack(): ui.Label(label_name, name=f"attribute_bool", width=self.label_width, height=20) name = "checked" if default_value else "unchecked" image =ui.Image(name=name, fill_policy=ui.FillPolicy.PRESERVE_ASPECT_FIT, height=18, width=18) ui.Spacer() rect_changed, rect_default = self.__build_value_changed_widget() image.set_mouse_pressed_fn(lambda x, y, b, m: _on_value_changed(image, rect_changed, rect_default)) # add call back to click the rect_changed to restore the default value rect_changed.set_mouse_pressed_fn(lambda x, y, b, m: _restore_default(rect_changed, rect_default)) def __build_value_changed_widget(self): with ui.VStack(width=20): ui.Spacer(height=3) rect_changed = ui.Rectangle(name="attribute_changed", width=15, height=15, visible= False) ui.Spacer(height=4) with ui.HStack(): ui.Spacer(width=3) rect_default = ui.Rectangle(name="attribute_default", width=5, height=5, visible= True) return rect_changed, rect_default def _build_gradient_float_slider(self, label_name, default_value=0, min=0, max=1): def _on_value_changed(model, rect_changed, rect_defaul): if model.as_float == default_value: rect_changed.visible = False rect_defaul.visible = True else: rect_changed.visible = True rect_defaul.visible = False def _restore_default(slider): slider.model.set_value(default_value) with ui.HStack(): ui.Label(label_name, name=f"attribute_name", width=self.label_width) with ui.ZStack(): button_background_gradient = build_gradient_image(cls_button_gradient, 22, "button_background_gradient") with ui.VStack(): ui.Spacer(height=1.5) with ui.HStack(): slider = ui.FloatSlider(name="float_slider", height=0, min=min, max=max) slider.model.set_value(default_value) ui.Spacer(width=1.5) ui.Spacer(width=4) rect_changed, rect_default = self.__build_value_changed_widget() # switch the visibility of the rect_changed and rect_default to indicate value changes slider.model.add_value_changed_fn(lambda model: _on_value_changed(model, rect_changed, rect_default)) # add call back to click the rect_changed to restore the default value rect_changed.set_mouse_pressed_fn(lambda x, y, b, m: _restore_default(slider)) return button_background_gradient def _build_combobox(self, label_name, options): def _on_value_changed(model, rect_changed, rect_defaul): index = model.get_item_value_model().get_value_as_int() if index == 0: rect_changed.visible = False rect_defaul.visible = True else: rect_changed.visible = True rect_defaul.visible = False def _restore_default(combo_box): combo_box.model.get_item_value_model().set_value(0) with ui.HStack(): ui.Label(label_name, name=f"attribute_name", width=self.label_width) with ui.ZStack(): ui.Image(name="combobox", fill_policy=ui.FillPolicy.STRETCH, height=35) with ui.HStack(): ui.Spacer(width=10) with ui.VStack(): ui.Spacer(height=10) option_list = list(options) combo_box = ui.ComboBox(0, *option_list, name="dropdown_menu") with ui.VStack(width=0): ui.Spacer(height=10) rect_changed, rect_default = self.__build_value_changed_widget() # switch the visibility of the rect_changed and rect_default to indicate value changes combo_box.model.add_item_changed_fn(lambda m, i: _on_value_changed(m, rect_changed, rect_default)) # add call back to click the rect_changed to restore the default value rect_changed.set_mouse_pressed_fn(lambda x, y, b, m: _restore_default(combo_box))

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_gradient_window/omni/example/ui_gradient_window/tests/test_window.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["TestWindow"] from omni.example.ui_gradient_window import PropertyWindowExample from omni.ui.tests.test_base import OmniUiTest from pathlib import Path import omni.kit.app import omni.kit.test EXTENSION_FOLDER_PATH = Path(omni.kit.app.get_app().get_extension_manager().get_extension_path_by_module(__name__)) TEST_DATA_PATH = EXTENSION_FOLDER_PATH.joinpath("data/tests") class TestWindow(OmniUiTest): async def test_general(self): """Testing general look of section""" window = PropertyWindowExample("Test") await omni.kit.app.get_app().next_update_async() await self.docked_test_window( window=window, width=450, height=600, ) # Wait for images for _ in range(20): await omni.kit.app.get_app().next_update_async() await self.finalize_test(golden_img_dir=TEST_DATA_PATH, golden_img_name="window.png")

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_julia_modeler/omni/example/ui_julia_modeler/custom_radio_collection.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["CustomRadioCollection"] from typing import List, Optional import omni.ui as ui from .style import ATTR_LABEL_WIDTH SPACING = 5 class CustomRadioCollection: """A custom collection of radio buttons. The group_name is on the first line, and each label and radio button are on subsequent lines. This one does not inherit from CustomBaseWidget because it doesn't have the same Head label, and doesn't have a Revert button at the end. """ def __init__(self, group_name: str, labels: List[str], model: ui.AbstractItemModel = None, default_value: bool = True, **kwargs): self.__group_name = group_name self.__labels = labels self.__default_val = default_value self.__images = [] self.__selection_model = ui.SimpleIntModel(default_value) self.__frame = ui.Frame() with self.__frame: self._build_fn() def destroy(self): self.__images = [] self.__selection_model = None self.__frame = None @property def model(self) -> Optional[ui.AbstractValueModel]: """The widget's model""" if self.__selection_model: return self.__selection_model @model.setter def model(self, value: int): """The widget's model""" self.__selection_model.set(value) def __getattr__(self, attr): """ Pretend it's self.__frame, so we have access to width/height and callbacks. """ return getattr(self.__frame, attr) def _on_value_changed(self, index: int = 0): """Set states of all radio buttons so only one is On.""" self.__selection_model.set_value(index) for i, img in enumerate(self.__images): img.checked = i == index img.name = "radio_on" if img.checked else "radio_off" def _build_fn(self): """Main meat of the widget. Draw the group_name label, label and radio button for each row, and set up callbacks to keep them updated. """ with ui.VStack(spacing=SPACING): ui.Spacer(height=2) ui.Label(self.__group_name.upper(), name="radio_group_name", width=ATTR_LABEL_WIDTH) for i, label in enumerate(self.__labels): with ui.HStack(): ui.Label(label, name="attribute_name", width=ATTR_LABEL_WIDTH) with ui.HStack(): with ui.VStack(): ui.Spacer(height=2) self.__images.append( ui.Image( name=("radio_on" if self.__default_val == i else "radio_off"), fill_policy=ui.FillPolicy.PRESERVE_ASPECT_FIT, height=16, width=16, checked=self.__default_val ) ) ui.Spacer() ui.Spacer(height=2) # Set up a mouse click callback for each radio button image for i in range(len(self.__labels)): self.__images[i].set_mouse_pressed_fn( lambda x, y, b, m, i=i: self._on_value_changed(i))

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_julia_modeler/omni/example/ui_julia_modeler/custom_bool_widget.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["CustomBoolWidget"] import omni.ui as ui from .custom_base_widget import CustomBaseWidget class CustomBoolWidget(CustomBaseWidget): """A custom checkbox or switch widget""" def __init__(self, model: ui.AbstractItemModel = None, default_value: bool = True, **kwargs): self.__default_val = default_value self.__bool_image = None # Call at the end, rather than start, so build_fn runs after all the init stuff CustomBaseWidget.__init__(self, model=model, **kwargs) def destroy(self): CustomBaseWidget.destroy() self.__bool_image = None def _restore_default(self): """Restore the default value.""" if self.revert_img.enabled: self.__bool_image.checked = self.__default_val self.__bool_image.name = ( "checked" if self.__bool_image.checked else "unchecked" ) self.revert_img.enabled = False def _on_value_changed(self): """Swap checkbox images and set revert_img to correct state.""" self.__bool_image.checked = not self.__bool_image.checked self.__bool_image.name = ( "checked" if self.__bool_image.checked else "unchecked" ) self.revert_img.enabled = self.__default_val != self.__bool_image.checked def _build_body(self): """Main meat of the widget. Draw the appropriate checkbox image, and set up callback. """ with ui.HStack(): with ui.VStack(): # Just shift the image down slightly (2 px) so it's aligned the way # all the other rows are. ui.Spacer(height=2) self.__bool_image = ui.Image( name="checked" if self.__default_val else "unchecked", fill_policy=ui.FillPolicy.PRESERVE_ASPECT_FIT, height=16, width=16, checked=self.__default_val ) # Let this spacer take up the rest of the Body space. ui.Spacer() self.__bool_image.set_mouse_pressed_fn( lambda x, y, b, m: self._on_value_changed())

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_julia_modeler/omni/example/ui_julia_modeler/custom_multifield_widget.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["CustomMultifieldWidget"] from typing import List, Optional import omni.ui as ui from .custom_base_widget import CustomBaseWidget class CustomMultifieldWidget(CustomBaseWidget): """A custom multifield widget with a variable number of fields, and customizable sublabels. """ def __init__(self, model: ui.AbstractItemModel = None, sublabels: Optional[List[str]] = None, default_vals: Optional[List[float]] = None, **kwargs): self.__field_labels = sublabels or ["X", "Y", "Z"] self.__default_vals = default_vals or [0.0] * len(self.__field_labels) self.__multifields = [] # Call at the end, rather than start, so build_fn runs after all the init stuff CustomBaseWidget.__init__(self, model=model, **kwargs) def destroy(self): CustomBaseWidget.destroy() self.__multifields = [] @property def model(self, index: int = 0) -> Optional[ui.AbstractItemModel]: """The widget's model""" if self.__multifields: return self.__multifields[index].model @model.setter def model(self, value: ui.AbstractItemModel, index: int = 0): """The widget's model""" self.__multifields[index].model = value def _restore_default(self): """Restore the default values.""" if self.revert_img.enabled: for i in range(len(self.__multifields)): model = self.__multifields[i].model model.as_float = self.__default_vals[i] self.revert_img.enabled = False def _on_value_changed(self, val_model: ui.SimpleFloatModel, index: int): """Set revert_img to correct state.""" val = val_model.as_float self.revert_img.enabled = self.__default_vals[index] != val def _build_body(self): """Main meat of the widget. Draw the multiple Fields with their respective labels, and set up callbacks to keep them updated. """ with ui.HStack(): for i, (label, val) in enumerate(zip(self.__field_labels, self.__default_vals)): with ui.HStack(spacing=3): ui.Label(label, name="multi_attr_label", width=0) model = ui.SimpleFloatModel(val) # TODO: Hopefully fix height after Field padding bug is merged! self.__multifields.append( ui.FloatField(model=model, name="multi_attr_field")) if i < len(self.__default_vals) - 1: # Only put space between fields and not after the last one ui.Spacer(width=15) for i, f in enumerate(self.__multifields): f.model.add_value_changed_fn(lambda v: self._on_value_changed(v, i))

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_julia_modeler/omni/example/ui_julia_modeler/custom_color_widget.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["CustomColorWidget"] from ctypes import Union import re from typing import List, Optional import omni.ui as ui from .custom_base_widget import CustomBaseWidget from .style import BLOCK_HEIGHT COLOR_PICKER_WIDTH = ui.Percent(35) FIELD_WIDTH = ui.Percent(65) COLOR_WIDGET_NAME = "color_block" SPACING = 4 class CustomColorWidget(CustomBaseWidget): """The compound widget for color input. The color picker widget model converts its 3 RGB values into a comma-separated string, to display in the StringField. And vice-versa. """ def __init__(self, *args, model=None, **kwargs): self.__defaults: List[Union[float, int]] = [a for a in args if a is not None] self.__strfield: Optional[ui.StringField] = None self.__colorpicker: Optional[ui.ColorWidget] = None self.__color_sub = None self.__strfield_sub = None # Call at the end, rather than start, so build_fn runs after all the init stuff CustomBaseWidget.__init__(self, model=model, **kwargs) def destroy(self): CustomBaseWidget.destroy() self.__strfield = None self.__colorpicker = None self.__color_sub = None self.__strfield_sub = None @property def model(self) -> Optional[ui.AbstractItemModel]: """The widget's model""" if self.__colorpicker: return self.__colorpicker.model @model.setter def model(self, value: ui.AbstractItemModel): """The widget's model""" self.__colorpicker.model = value @staticmethod def simplify_str(val): s = str(round(val, 3)) s_clean = re.sub(r'0*$', '', s) # clean trailing 0's s_clean = re.sub(r'[.]$', '', s_clean) # clean trailing . s_clean = re.sub(r'^0', '', s_clean) # clean leading 0 return s_clean def set_color_stringfield(self, item_model: ui.AbstractItemModel, children: List[ui.AbstractItem]): """Take the colorpicker model that has 3 child RGB values, convert them to a comma-separated string, and set the StringField value to that string. Args: item_model: Colorpicker model children: child Items of the colorpicker """ field_str = ", ".join([self.simplify_str(item_model.get_item_value_model(c).as_float) for c in children]) self.__strfield.model.set_value(field_str) if self.revert_img: self._on_value_changed() def set_color_widget(self, str_model: ui.SimpleStringModel, children: List[ui.AbstractItem]): """Parse the new StringField value and set the ui.ColorWidget component items to the new values. Args: str_model: SimpleStringModel for the StringField children: Child Items of the ui.ColorWidget's model """ joined_str = str_model.get_value_as_string() for model, comp_str in zip(children, joined_str.split(",")): comp_str_clean = comp_str.strip() try: self.__colorpicker.model.get_item_value_model(model).as_float = float(comp_str_clean) except ValueError: # Usually happens in the middle of typing pass def _on_value_changed(self, *args): """Set revert_img to correct state.""" default_str = ", ".join([self.simplify_str(val) for val in self.__defaults]) cur_str = self.__strfield.model.as_string self.revert_img.enabled = default_str != cur_str def _restore_default(self): """Restore the default values.""" if self.revert_img.enabled: field_str = ", ".join([self.simplify_str(val) for val in self.__defaults]) self.__strfield.model.set_value(field_str) self.revert_img.enabled = False def _build_body(self): """Main meat of the widget. Draw the colorpicker, stringfield, and set up callbacks to keep them updated. """ with ui.HStack(spacing=SPACING): # The construction of the widget depends on what the user provided, # defaults or a model if self.existing_model: # the user provided a model self.__colorpicker = ui.ColorWidget( self.existing_model, width=COLOR_PICKER_WIDTH, height=BLOCK_HEIGHT, name=COLOR_WIDGET_NAME ) color_model = self.existing_model else: # the user provided a list of default values self.__colorpicker = ui.ColorWidget( *self.__defaults, width=COLOR_PICKER_WIDTH, height=BLOCK_HEIGHT, name=COLOR_WIDGET_NAME ) color_model = self.__colorpicker.model self.__strfield = ui.StringField(width=FIELD_WIDTH, name="attribute_color") self.__color_sub = self.__colorpicker.model.subscribe_item_changed_fn( lambda m, _, children=color_model.get_item_children(): self.set_color_stringfield(m, children)) self.__strfield_sub = self.__strfield.model.subscribe_value_changed_fn( lambda m, children=color_model.get_item_children(): self.set_color_widget(m, children)) # show data at the start self.set_color_stringfield(self.__colorpicker.model, children=color_model.get_item_children())

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_julia_modeler/omni/example/ui_julia_modeler/custom_path_button.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["CustomPathButtonWidget"] from typing import Callable, Optional import omni.ui as ui from .style import ATTR_LABEL_WIDTH, BLOCK_HEIGHT class CustomPathButtonWidget: """A compound widget for holding a path in a StringField, and a button that can perform an action. TODO: Get text ellision working in the path field, to start with "..." """ def __init__(self, label: str, path: str, btn_label: str, btn_callback: Callable): self.__attr_label = label self.__pathfield: ui.StringField = None self.__path = path self.__btn_label = btn_label self.__btn = None self.__callback = btn_callback self.__frame = ui.Frame() with self.__frame: self._build_fn() def destroy(self): self.__pathfield = None self.__btn = None self.__callback = None self.__frame = None @property def model(self) -> Optional[ui.AbstractItem]: """The widget's model""" if self.__pathfield: return self.__pathfield.model @model.setter def model(self, value: ui.AbstractItem): """The widget's model""" self.__pathfield.model = value def get_path(self): return self.model.as_string def _build_fn(self): """Draw all of the widget parts and set up callbacks.""" with ui.HStack(): ui.Label( self.__attr_label, name="attribute_name", width=ATTR_LABEL_WIDTH ) self.__pathfield = ui.StringField( name="path_field", height=BLOCK_HEIGHT, width=ui.Fraction(2), ) # TODO: Add clippingType=ELLIPSIS_LEFT for long paths self.__pathfield.model.set_value(self.__path) self.__btn = ui.Button( self.__btn_label, name="tool_button", height=BLOCK_HEIGHT, width=ui.Fraction(1), clicked_fn=lambda path=self.get_path(): self.__callback(path), )

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_julia_modeler/omni/example/ui_julia_modeler/custom_slider_widget.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["CustomSliderWidget"] from typing import Optional import omni.ui as ui from omni.ui import color as cl from omni.ui import constant as fl from .custom_base_widget import CustomBaseWidget NUM_FIELD_WIDTH = 50 SLIDER_WIDTH = ui.Percent(100) FIELD_HEIGHT = 22 # TODO: Once Field padding is fixed, this should be 18 SPACING = 4 TEXTURE_NAME = "slider_bg_texture" class CustomSliderWidget(CustomBaseWidget): """A compound widget for scalar slider input, which contains a Slider and a Field with text input next to it. """ def __init__(self, model: ui.AbstractItemModel = None, num_type: str = "float", min=0.0, max=1.0, default_val=0.0, display_range: bool = False, **kwargs): self.__slider: Optional[ui.AbstractSlider] = None self.__numberfield: Optional[ui.AbstractField] = None self.__min = min self.__max = max self.__default_val = default_val self.__num_type = num_type self.__display_range = display_range # Call at the end, rather than start, so build_fn runs after all the init stuff CustomBaseWidget.__init__(self, model=model, **kwargs) def destroy(self): CustomBaseWidget.destroy() self.__slider = None self.__numberfield = None @property def model(self) -> Optional[ui.AbstractItemModel]: """The widget's model""" if self.__slider: return self.__slider.model @model.setter def model(self, value: ui.AbstractItemModel): """The widget's model""" self.__slider.model = value self.__numberfield.model = value def _on_value_changed(self, *args): """Set revert_img to correct state.""" if self.__num_type == "float": index = self.model.as_float else: index = self.model.as_int self.revert_img.enabled = self.__default_val != index def _restore_default(self): """Restore the default value.""" if self.revert_img.enabled: self.model.set_value(self.__default_val) self.revert_img.enabled = False def _build_display_range(self): """Builds just the tiny text range under the slider.""" with ui.HStack(): ui.Label(str(self.__min), alignment=ui.Alignment.LEFT, name="range_text") if self.__min < 0 and self.__max > 0: # Add middle value (always 0), but it may or may not be centered, # depending on the min/max values. total_range = self.__max - self.__min # subtract 25% to account for end number widths left = 100 * abs(0 - self.__min) / total_range - 25 right = 100 * abs(self.__max - 0) / total_range - 25 ui.Spacer(width=ui.Percent(left)) ui.Label("0", alignment=ui.Alignment.CENTER, name="range_text") ui.Spacer(width=ui.Percent(right)) else: ui.Spacer() ui.Label(str(self.__max), alignment=ui.Alignment.RIGHT, name="range_text") ui.Spacer(height=.75) def _build_body(self): """Main meat of the widget. Draw the Slider, display range text, Field, and set up callbacks to keep them updated. """ with ui.HStack(spacing=0): # the user provided a list of default values with ui.VStack(spacing=3, width=ui.Fraction(3)): with ui.ZStack(): # Put texture image here, with rounded corners, then make slider # bg be fully transparent, and fg be gray and partially transparent with ui.Frame(width=SLIDER_WIDTH, height=FIELD_HEIGHT, horizontal_clipping=True): # Spacing is negative because "tileable" texture wasn't # perfectly tileable, so that adds some overlap to line up better. with ui.HStack(spacing=-12): for i in range(50): # tiling the texture ui.Image(name=TEXTURE_NAME, fill_policy=ui.FillPolicy.PRESERVE_ASPECT_CROP, width=50,) slider_cls = ( ui.FloatSlider if self.__num_type == "float" else ui.IntSlider ) self.__slider = slider_cls( height=FIELD_HEIGHT, min=self.__min, max=self.__max, name="attr_slider" ) if self.__display_range: self._build_display_range() with ui.VStack(width=ui.Fraction(1)): model = self.__slider.model model.set_value(self.__default_val) field_cls = ( ui.FloatField if self.__num_type == "float" else ui.IntField ) # Note: This is a hack to allow for text to fill the Field space more, as there was a bug # with Field padding. It is fixed, and will be available in the next release of Kit. with ui.ZStack(): # height=FIELD_HEIGHT-1 to account for the border, so the field isn't # slightly taller than the slider ui.Rectangle( style_type_name_override="Field", name="attr_field", height=FIELD_HEIGHT - 1 ) with ui.HStack(height=0): ui.Spacer(width=2) self.__numberfield = field_cls( model, height=0, style={ "background_color": cl.transparent, "border_color": cl.transparent, "padding": 4, "font_size": fl.field_text_font_size, }, ) if self.__display_range: ui.Spacer() model.add_value_changed_fn(self._on_value_changed)

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_julia_modeler/omni/example/ui_julia_modeler/window.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["JuliaModelerWindow"] import omni.ui as ui from omni.kit.window.popup_dialog import MessageDialog from .custom_bool_widget import CustomBoolWidget from .custom_color_widget import CustomColorWidget from .custom_combobox_widget import CustomComboboxWidget from .custom_multifield_widget import CustomMultifieldWidget from .custom_path_button import CustomPathButtonWidget from .custom_radio_collection import CustomRadioCollection from .custom_slider_widget import CustomSliderWidget from .style import julia_modeler_style, ATTR_LABEL_WIDTH SPACING = 5 class JuliaModelerWindow(ui.Window): """The class that represents the window""" def __init__(self, title: str, delegate=None, **kwargs): self.__label_width = ATTR_LABEL_WIDTH super().__init__(title, **kwargs) # Apply the style to all the widgets of this window self.frame.style = julia_modeler_style # Set the function that is called to build widgets when the window is # visible self.frame.set_build_fn(self._build_fn) def destroy(self): # Destroys all the children super().destroy() @property def label_width(self): """The width of the attribute label""" return self.__label_width @label_width.setter def label_width(self, value): """The width of the attribute label""" self.__label_width = value self.frame.rebuild() def on_export_btn_click(self, path): """Sample callback that is used when the Export button is pressed.""" dialog = MessageDialog( title="Button Pressed Dialog", message=f"Export Button was clicked with path inside: {path}", disable_cancel_button=True, ok_handler=lambda dialog: dialog.hide() ) dialog.show() def _build_title(self): with ui.VStack(): ui.Spacer(height=10) ui.Label("JULIA QUATERNION MODELER - 1.0", name="window_title") ui.Spacer(height=10) def _build_collapsable_header(self, collapsed, title): """Build a custom title of CollapsableFrame""" with ui.VStack(): ui.Spacer(height=8) with ui.HStack(): ui.Label(title, name="collapsable_name") if collapsed: image_name = "collapsable_opened" else: image_name = "collapsable_closed" ui.Image(name=image_name, width=10, height=10) ui.Spacer(height=8) ui.Line(style_type_name_override="HeaderLine") def _build_calculations(self): """Build the widgets of the "Calculations" group""" with ui.CollapsableFrame("Calculations".upper(), name="group", build_header_fn=self._build_collapsable_header): with ui.VStack(height=0, spacing=SPACING): ui.Spacer(height=6) CustomSliderWidget(min=0, max=20, num_type="int", label="Precision", default_val=6) CustomSliderWidget(min=0, max=20, num_type="int", label="Iterations", default_val=10) def _build_parameters(self): """Build the widgets of the "Parameters" group""" with ui.CollapsableFrame("Parameters".upper(), name="group", build_header_fn=self._build_collapsable_header): with ui.VStack(height=0, spacing=SPACING): ui.Spacer(height=6) CustomSliderWidget(min=-2, max=2, display_range=True, label="Iterations", default_val=0.75) CustomSliderWidget(min=0, max=2, display_range=True, label="i", default_val=0.65) CustomSliderWidget(min=0, max=2, display_range=True, label="j", default_val=0.25) CustomSliderWidget(min=0, max=2, display_range=True, label="k", default_val=0.55) CustomSliderWidget(min=0, max=3.14, display_range=True, label="Theta", default_val=1.25) def _build_light_1(self): """Build the widgets of the "Light 1" group""" with ui.CollapsableFrame("Light 1".upper(), name="group", build_header_fn=self._build_collapsable_header): with ui.VStack(height=0, spacing=SPACING): ui.Spacer(height=6) CustomMultifieldWidget( label="Orientation", default_vals=[0.0, 0.0, 0.0] ) CustomSliderWidget(min=0, max=1.75, label="Intensity", default_val=1.75) CustomColorWidget(1.0, 0.875, 0.5, label="Color") CustomBoolWidget(label="Shadow", default_value=True) CustomSliderWidget(min=0, max=2, label="Shadow Softness", default_val=.1) def _build_scene(self): """Build the widgets of the "Scene" group""" with ui.CollapsableFrame("Scene".upper(), name="group", build_header_fn=self._build_collapsable_header): with ui.VStack(height=0, spacing=SPACING): ui.Spacer(height=6) CustomSliderWidget(min=0, max=160, display_range=True, num_type="int", label="Field of View", default_val=60) CustomMultifieldWidget( label="Orientation", default_vals=[0.0, 0.0, 0.0] ) CustomSliderWidget(min=0, max=2, label="Camera Distance", default_val=.1) CustomBoolWidget(label="Antialias", default_value=False) CustomBoolWidget(label="Ambient Occlusion", default_value=True) CustomMultifieldWidget( label="Ambient Distance", sublabels=["Min", "Max"], default_vals=[0.0, 200.0] ) CustomComboboxWidget(label="Ambient Falloff", options=["Linear", "Quadratic", "Cubic"]) CustomColorWidget(.6, 0.62, 0.9, label="Background Color") CustomRadioCollection("Render Method", labels=["Path Traced", "Volumetric"], default_value=1) CustomPathButtonWidget( label="Export Path", path=".../export/mesh1.usd", btn_label="Export", btn_callback=self.on_export_btn_click, ) ui.Spacer(height=10) def _build_fn(self): """ The method that is called to build all the UI once the window is visible. """ with ui.ScrollingFrame(name="window_bg", horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_OFF): with ui.VStack(height=0): self._build_title() self._build_calculations() self._build_parameters() self._build_light_1() self._build_scene()

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_window/omni/example/ui_window/style.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["example_window_style"] from omni.ui import color as cl from omni.ui import constant as fl from omni.ui import url import omni.kit.app import omni.ui as ui import pathlib EXTENSION_FOLDER_PATH = pathlib.Path( omni.kit.app.get_app().get_extension_manager().get_extension_path_by_module(__name__) ) # Pre-defined constants. It's possible to change them runtime. cl.example_window_attribute_bg = cl("#1f2124") cl.example_window_attribute_fg = cl("#0f1115") cl.example_window_hovered = cl("#FFFFFF") cl.example_window_text = cl("#CCCCCC") fl.example_window_attr_hspacing = 10 fl.example_window_attr_spacing = 1 fl.example_window_group_spacing = 2 url.example_window_icon_closed = f"{EXTENSION_FOLDER_PATH}/data/closed.svg" url.example_window_icon_opened = f"{EXTENSION_FOLDER_PATH}/data/opened.svg" # The main style dict example_window_style = { "Label::attribute_name": { "alignment": ui.Alignment.RIGHT_CENTER, "margin_height": fl.example_window_attr_spacing, "margin_width": fl.example_window_attr_hspacing, }, "Label::attribute_name:hovered": {"color": cl.example_window_hovered}, "Label::collapsable_name": {"alignment": ui.Alignment.LEFT_CENTER}, "Slider::attribute_int:hovered": {"color": cl.example_window_hovered}, "Slider": { "background_color": cl.example_window_attribute_bg, "draw_mode": ui.SliderDrawMode.HANDLE, }, "Slider::attribute_float": { "draw_mode": ui.SliderDrawMode.FILLED, "secondary_color": cl.example_window_attribute_fg, }, "Slider::attribute_float:hovered": {"color": cl.example_window_hovered}, "Slider::attribute_vector:hovered": {"color": cl.example_window_hovered}, "Slider::attribute_color:hovered": {"color": cl.example_window_hovered}, "CollapsableFrame::group": {"margin_height": fl.example_window_group_spacing}, "Image::collapsable_opened": {"color": cl.example_window_text, "image_url": url.example_window_icon_opened}, "Image::collapsable_closed": {"color": cl.example_window_text, "image_url": url.example_window_icon_closed}, }

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_window/omni/example/ui_window/color_widget.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["ColorWidget"] from ctypes import Union from typing import List, Optional import omni.ui as ui COLOR_PICKER_WIDTH = 20 SPACING = 4 class ColorWidget: """The compound widget for color input""" def __init__(self, *args, model=None, **kwargs): self.__defaults: List[Union[float, int]] = args self.__model: Optional[ui.AbstractItemModel] = kwargs.pop("model", None) self.__multifield: Optional[ui.MultiFloatDragField] = None self.__colorpicker: Optional[ui.ColorWidget] = None self.__frame = ui.Frame() with self.__frame: self._build_fn() def destroy(self): self.__model = None self.__multifield = None self.__colorpicker = None self.__frame = None def __getattr__(self, attr): """ Pretend it's self.__frame, so we have access to width/height and callbacks. """ return getattr(self.__root_frame, attr) @property def model(self) -> Optional[ui.AbstractItemModel]: """The widget's model""" if self.__multifield: return self.__multifield.model @model.setter def model(self, value: ui.AbstractItemModel): """The widget's model""" self.__multifield.model = value self.__colorpicker.model = value def _build_fn(self): with ui.HStack(spacing=SPACING): # The construction of multi field depends on what the user provided, # defaults or a model if self.__model: # the user provided a model self.__multifield = ui.MultiFloatDragField( min=0, max=1, model=self.__model, h_spacing=SPACING, name="attribute_color" ) model = self.__model else: # the user provided a list of default values self.__multifield = ui.MultiFloatDragField( *self.__defaults, min=0, max=1, h_spacing=SPACING, name="attribute_color" ) model = self.__multifield.model self.__colorpicker = ui.ColorWidget(model, width=COLOR_PICKER_WIDTH)

Python

NVIDIA-Omniverse/kit-extension-sample-ui-window/exts/omni.example.ui_window/omni/example/ui_window/window.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. # __all__ = ["ExampleWindow"] import omni.ui as ui from .style import example_window_style from .color_widget import ColorWidget LABEL_WIDTH = 120 SPACING = 4 class ExampleWindow(ui.Window): """The class that represents the window""" def __init__(self, title: str, delegate=None, **kwargs): self.__label_width = LABEL_WIDTH super().__init__(title, **kwargs) # Apply the style to all the widgets of this window self.frame.style = example_window_style # Set the function that is called to build widgets when the window is # visible self.frame.set_build_fn(self._build_fn) def destroy(self): # It will destroy all the children super().destroy() @property def label_width(self): """The width of the attribute label""" return self.__label_width @label_width.setter def label_width(self, value): """The width of the attribute label""" self.__label_width = value self.frame.rebuild() def _build_collapsable_header(self, collapsed, title): """Build a custom title of CollapsableFrame""" with ui.HStack(): ui.Label(title, name="collapsable_name") if collapsed: image_name = "collapsable_opened" else: image_name = "collapsable_closed" ui.Image(name=image_name, width=20, height=20) def _build_calculations(self): """Build the widgets of the "Calculations" group""" with ui.CollapsableFrame("Calculations", name="group", build_header_fn=self._build_collapsable_header): with ui.VStack(height=0, spacing=SPACING): with ui.HStack(): ui.Label("Precision", name="attribute_name", width=self.label_width) ui.IntSlider(name="attribute_int") with ui.HStack(): ui.Label("Iterations", name="attribute_name", width=self.label_width) ui.IntSlider(name="attribute_int", min=0, max=5) def _build_parameters(self): """Build the widgets of the "Parameters" group""" with ui.CollapsableFrame("Parameters", name="group", build_header_fn=self._build_collapsable_header): with ui.VStack(height=0, spacing=SPACING): with ui.HStack(): ui.Label("Value", name="attribute_name", width=self.label_width) ui.FloatSlider(name="attribute_float") with ui.HStack(): ui.Label("i", name="attribute_name", width=self.label_width) ui.FloatSlider(name="attribute_float", min=-1, max=1) with ui.HStack(): ui.Label("j", name="attribute_name", width=self.label_width) ui.FloatSlider(name="attribute_float", min=-1, max=1) with ui.HStack(): ui.Label("k", name="attribute_name", width=self.label_width) ui.FloatSlider(name="attribute_float", min=-1, max=1) with ui.HStack(): ui.Label("Theta", name="attribute_name", width=self.label_width) ui.FloatSlider(name="attribute_float") def _build_light_1(self): """Build the widgets of the "Light 1" group""" with ui.CollapsableFrame("Light 1", name="group", build_header_fn=self._build_collapsable_header): with ui.VStack(height=0, spacing=SPACING): with ui.HStack(): ui.Label("Orientation", name="attribute_name", width=self.label_width) ui.MultiFloatDragField(0.0, 0.0, 0.0, h_spacing=SPACING, name="attribute_vector") with ui.HStack(): ui.Label("Intensity", name="attribute_name", width=self.label_width) ui.FloatSlider(name="attribute_float") with ui.HStack(): ui.Label("Color", name="attribute_name", width=self.label_width) # The custom compound widget ColorWidget(0.25, 0.5, 0.75) with ui.HStack(): ui.Label("Shadow", name="attribute_name", width=self.label_width) ui.CheckBox(name="attribute_bool") def _build_fn(self): """ The method that is called to build all the UI once the window is visible. """ with ui.ScrollingFrame(): with ui.VStack(height=0): self._build_calculations() self._build_parameters() self._build_light_1()

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.sample.deepsearchpicker/omni/sample/deepsearchpicker/style.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.ui as ui from pathlib import Path icons_path = Path(__file__).parent.parent.parent.parent / "icons" gen_ai_style = { "HStack": { "margin": 3 }, "Button.Image::create": {"image_url": f"{icons_path}/plus.svg", "color": 0xFF00B976} }

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.sample.deepsearchpicker/omni/sample/deepsearchpicker/extension.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.ext from .window import DeepSearchPickerWindow # Any class derived from `omni.ext.IExt` in top level module (defined in `python.modules` of `extension.toml`) will be # instantiated when extension gets enabled and `on_startup(ext_id)` will be called. Later when extension gets disabled # on_shutdown() is called. class MyExtension(omni.ext.IExt): # ext_id is current extension id. It can be used with extension manager to query additional information, like where # this extension is located on filesystem. def on_startup(self, ext_id): self._window = DeepSearchPickerWindow("DeepSearch Swap", width=300, height=300) def on_shutdown(self): self._window.destroy() self._window = None

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.sample.deepsearchpicker/omni/sample/deepsearchpicker/deep_search.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from omni.kit.ngsearch.client import NGSearchClient import carb import asyncio class deep_search(): async def query_items(queries, url: str, paths): result = list(tuple()) for query in queries: query_result = await deep_search._query_first(query, url, paths) if query_result is not None: result.append(query_result) return result async def _query_first(query: str, url: str, paths): filtered_query = "ext:usd,usdz,usda path:" for path in paths: filtered_query = filtered_query + "\"" + str(path) + "\"," filtered_query = filtered_query[:-1] filtered_query = filtered_query + " " filtered_query = filtered_query + query SearchResult = await NGSearchClient.get_instance().find2( query=filtered_query, url=url) if len(SearchResult.paths) > 0: return (query, SearchResult.paths[0].uri) else: return None async def query_all(query: str, url: str, paths): filtered_query = "ext:usd,usdz,usda path:" for path in paths: filtered_query = filtered_query + "\"" + str(path) + "\"," filtered_query = filtered_query[:-1] filtered_query = filtered_query + " " filtered_query = filtered_query + query return await NGSearchClient.get_instance().find2(query=filtered_query, url=url)

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.sample.deepsearchpicker/omni/sample/deepsearchpicker/window.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.ui as ui import omni.usd import carb from .style import gen_ai_style from .deep_search import deep_search import asyncio from pxr import UsdGeom, Usd, Sdf, Gf class DeepSearchPickerWindow(ui.Window): def __init__(self, title: str, **kwargs) -> None: super().__init__(title, **kwargs) # Models self.frame.set_build_fn(self._build_fn) self._index = 0 self._query_results = None self._selected_prim = None self._prim_path_model = ui.SimpleStringModel() def _build_fn(self): async def replace_prim(): self._index = 0 ctx = omni.usd.get_context() prim_paths = ctx.get_selection().get_selected_prim_paths() if len(prim_paths) != 1: carb.log_warn("You must select one and only one prim") return prim_path = prim_paths[0] stage = ctx.get_stage() self._selected_prim = stage.GetPrimAtPath(prim_path) query = self._selected_prim.GetAttribute("DeepSearch:Query").Get() prop_paths = ["/Projects/simready_content/common_assets/props/", "/NVIDIA/Assets/Isaac/2022.2.1/Isaac/Robots/", "/NVIDIA/Assets/Isaac/2022.1/NVIDIA/Assets/ArchVis/Residential/Furniture/"] self._query_results = await deep_search.query_all(query, "omniverse://ov-simready/", paths=prop_paths) self._prim_path_model.set_value(prim_path) def increment_prim_index(): if self._query_results is None: return self._index = self._index + 1 if self._index >= len(self._query_results.paths): self._index = 0 self.replace_reference() def decrement_prim_index(): if self._query_results is None: return self._index = self._index - 1 if self._index <= 0: self._index = len(self._query_results.paths) - 1 self.replace_reference() with self.frame: with ui.VStack(style=gen_ai_style): with ui.HStack(height=0): ui.Spacer() ui.StringField(model=self._prim_path_model, width=365, height=30) ui.Button(name="create", width=30, height=30, clicked_fn=lambda: asyncio.ensure_future(replace_prim())) ui.Spacer() with ui.HStack(height=0): ui.Spacer() ui.Button("<", width=200, clicked_fn=lambda: decrement_prim_index()) ui.Button(">", width=200, clicked_fn=lambda: increment_prim_index()) ui.Spacer() def replace_reference(self): references: Usd.references = self._selected_prim.GetReferences() references.ClearReferences() references.AddReference( assetPath="omniverse://ov-simready" + self._query_results.paths[self._index].uri) carb.log_info("Got it?") def destroy(self): super().destroy()

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.example.airoomgenerator/omni/example/airoomgenerator/priminfo.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pxr import Usd, Gf class PrimInfo: # Class that stores the prim info def __init__(self, prim: Usd.Prim, name: str = "") -> None: self.prim = prim self.child = prim.GetAllChildren()[0] self.length = self.GetLengthOfPrim() self.width = self.GetWidthOfPrim() self.origin = self.GetPrimOrigin() self.area_name = name def GetLengthOfPrim(self) -> str: # Returns the X value attr = self.child.GetAttribute('xformOp:scale') x_scale = attr.Get()[0] return str(x_scale) def GetWidthOfPrim(self) -> str: # Returns the Z value attr = self.child.GetAttribute('xformOp:scale') z_scale = attr.Get()[2] return str(z_scale) def GetPrimOrigin(self) -> str: attr = self.prim.GetAttribute('xformOp:translate') origin = Gf.Vec3d(0,0,0) if attr: origin = attr.Get() phrase = str(origin[0]) + ", " + str(origin[1]) + ", " + str(origin[2]) return phrase

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.example.airoomgenerator/omni/example/airoomgenerator/widgets.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.ui as ui from omni.ui import color as cl import asyncio import omni import carb class ProgressBar: def __init__(self): self.progress_bar_window = None self.left = None self.right = None self._build_fn() async def play_anim_forever(self): fraction = 0.0 while True: fraction = (fraction + 0.01) % 1.0 self.left.width = ui.Fraction(fraction) self.right.width = ui.Fraction(1.0-fraction) await omni.kit.app.get_app().next_update_async() def _build_fn(self): with ui.VStack(): self.progress_bar_window = ui.HStack(height=0, visible=False) with self.progress_bar_window: ui.Label("Processing", width=0, style={"margin_width": 3}) self.left = ui.Spacer(width=ui.Fraction(0.0)) ui.Rectangle(width=50, style={"background_color": cl("#76b900")}) self.right = ui.Spacer(width=ui.Fraction(1.0)) def show_bar(self, to_show): self.progress_bar_window.visible = to_show

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.example.airoomgenerator/omni/example/airoomgenerator/chatgpt_apiconnect.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import carb import aiohttp import asyncio from .prompts import system_input, user_input, assistant_input from .deep_search import query_items from .item_generator import place_greyboxes, place_deepsearch_results async def chatGPT_call(prompt: str): # Load your API key from an environment variable or secret management service settings = carb.settings.get_settings() apikey = settings.get_as_string("/persistent/exts/omni.example.airoomgenerator/APIKey") my_prompt = prompt.replace("\n", " ") # Send a request API try: parameters = { "model": "gpt-3.5-turbo", "messages": [ {"role": "system", "content": system_input}, {"role": "user", "content": user_input}, {"role": "assistant", "content": assistant_input}, {"role": "user", "content": my_prompt} ] } chatgpt_url = "https://api.openai.com/v1/chat/completions" headers = {"Authorization": "Bearer %s" % apikey} # Create a completion using the chatGPT model async with aiohttp.ClientSession() as session: async with session.post(chatgpt_url, headers=headers, json=parameters) as r: response = await r.json() text = response["choices"][0]["message"]['content'] except Exception as e: carb.log_error("An error as occurred") return None, str(e) # Parse data that was given from API try: #convert string to object data = json.loads(text) except ValueError as e: carb.log_error(f"Exception occurred: {e}") return None, text else: # Get area_objects_list object_list = data['area_objects_list'] return object_list, text async def call_Generate(prim_info, prompt, use_chatgpt, use_deepsearch, response_label, progress_widget): run_loop = asyncio.get_event_loop() progress_widget.show_bar(True) task = run_loop.create_task(progress_widget.play_anim_forever()) response = "" #chain the prompt area_name = prim_info.area_name.split("/World/Layout/") concat_prompt = area_name[-1].replace("_", " ") + ", " + prim_info.length + "x" + prim_info.width + ", origin at (0.0, 0.0, 0.0), generate a list of appropriate items in the correct places. " + prompt root_prim_path = "/World/Layout/GPT/" if prim_info.area_name != "": root_prim_path= prim_info.area_name + "/items/" if use_chatgpt: #when calling the API objects, response = await chatGPT_call(concat_prompt) else: #when testing and you want to skip the API call data = json.loads(assistant_input) objects = data['area_objects_list'] if objects is None: response_label.text = response return if use_deepsearch: settings = carb.settings.get_settings() nucleus_path = settings.get_as_string("/persistent/exts/omni.example.airoomgenerator/deepsearch_nucleus_path") filter_path = settings.get_as_string("/persistent/exts/omni.example.airoomgenerator/filter_path") filter_paths = filter_path.split(',') queries = list() for item in objects: queries.append(item['object_name']) query_result = await query_items(queries=queries, url=nucleus_path, paths=filter_paths) if query_result is not None: place_deepsearch_results( gpt_results=objects, query_result=query_result, root_prim_path=root_prim_path) else: place_greyboxes( gpt_results=objects, root_prim_path=root_prim_path) else: place_greyboxes( gpt_results=objects, root_prim_path=root_prim_path) task.cancel() await asyncio.sleep(1) response_label.text = response progress_widget.show_bar(False)

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.example.airoomgenerator/omni/example/airoomgenerator/style.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.ui as ui from pathlib import Path icons_path = Path(__file__).parent.parent.parent.parent / "icons" gen_ai_style = { "HStack": { "margin": 3 }, "Button.Image::create": {"image_url": f"{icons_path}/plus.svg", "color": 0xFF00B976}, "Button.Image::properties": {"image_url": f"{icons_path}/cog.svg", "color": 0xFF989898}, "Line": { "margin": 3 }, "Label": { "margin_width": 5 } } guide = """ Step 1: Create a Floor - You can draw a floor outline using the pencil tool. Right click in the viewport then `Create>BasicCurves>From Pencil` - OR Create a prim and scale it to the size you want. i.e. Right click in the viewport then `Create>Mesh>Cube`. - Next, with the floor selected type in a name into "Area Name". Make sure the area name is relative to the room you want to generate. For example, if you inputted the name as "bedroom" ChatGPT will be prompted that the room is a bedroom. - Then click the '+' button. This will generate the floor and add the option to our combo box. Step 2: Prompt - Type in a prompt that you want to send along to ChatGPT. This can be information about what is inside of the room. For example, "generate a comfortable reception area that contains a front desk and an area for guest to sit down". Step 3: Generate - Select 'use ChatGPT' if you want to recieve a response from ChatGPT otherwise it will use a premade response. - Select 'use Deepsearch' if you want to use the deepsearch functionality. (ENTERPRISE USERS ONLY) When deepsearch is false it will spawn in cubes that greybox the scene. - Hit Generate, after hitting generate it will start making the appropriate calls. Loading bar will be shown as api-calls are being made. Step 4: More Rooms - To add another room you can repeat Steps 1-3. To regenerate a previous room just select it from the 'Current Room' in the dropdown menu. - The dropdown menu will remember the last prompt you used to generate the items. - If you do not like the items it generated, you can hit the generate button until you are satisfied with the items. """

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.example.airoomgenerator/omni/example/airoomgenerator/prompts.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. system_input='''You are an area generator expert. Given an area of a certain size, you can generate a list of items that are appropriate to that area, in the right place, and with a representative material. You operate in a 3D Space. You work in a X,Y,Z coordinate system. X denotes width, Y denotes height, Z denotes depth. 0.0,0.0,0.0 is the default space origin. You receive from the user the name of the area, the size of the area on X and Z axis in centimetres, the origin point of the area (which is at the center of the area). You answer by only generating JSON files that contain the following information: - area_name: name of the area - X: coordinate of the area on X axis - Y: coordinate of the area on Y axis - Z: coordinate of the area on Z axis - area_size_X: dimension in cm of the area on X axis - area_size_Z: dimension in cm of the area on Z axis - area_objects_list: list of all the objects in the area For each object you need to store: - object_name: name of the object - X: coordinate of the object on X axis - Y: coordinate of the object on Y axis - Z: coordinate of the object on Z axis - Length: dimension in cm of the object on X axis - Width: dimension in cm of the object on Y axis - Height: dimension in cm of the object on Z axis - Material: a reasonable material of the object using an exact name from the following list: Plywood, Leather_Brown, Leather_Pumpkin, Leather_Black, Aluminum_Cast, Birch, Beadboard, Cardboard, Cloth_Black, Cloth_Gray, Concrete_Polished, Glazed_Glass, CorrugatedMetal, Cork, Linen_Beige, Linen_Blue, Linen_White, Mahogany, MDF, Oak, Plastic_ABS, Steel_Carbon, Steel_Stainless, Veneer_OU_Walnut, Veneer_UX_Walnut_Cherry, Veneer_Z5_Maple. Each object name should include an appropriate adjective. Keep in mind, objects should be disposed in the area to create the most meaningful layout possible, and they shouldn't overlap. All objects must be within the bounds of the area size; Never place objects further than 1/2 the length or 1/2 the depth of the area from the origin. Also keep in mind that the objects should be disposed all over the area in respect to the origin point of the area, and you can use negative values as well to display items correctly, since origin of the area is always at the center of the area. Remember, you only generate JSON code, nothing else. It's very important. ''' user_input="Warehouse, 1000x1000, origin at (0.0,0.0,0.0), generate a list of appropriate items in the correct places. Generate warehouse objects" assistant_input='''{ "area_name": "Warehouse_Area", "X": 0.0, "Y": 0.0, "Z": 0.0, "area_size_X": 1000, "area_size_Z": 1000, "area_objects_list": [ { "object_name": "Parts_Pallet_1", "X": -150, "Y": 0.0, "Z": 250, "Length": 100, "Width": 100, "Height": 10, "Material": "Plywood" }, { "object_name": "Boxes_Pallet_2", "X": -150, "Y": 0.0, "Z": 150, "Length": 100, "Width": 100, "Height": 10, "Material": "Plywood" }, { "object_name": "Industrial_Storage_Rack_1", "X": -150, "Y": 0.0, "Z": 50, "Length": 200, "Width": 50, "Height": 300, "Material": "Steel_Carbon" }, { "object_name": "Empty_Pallet_3", "X": -150, "Y": 0.0, "Z": -50, "Length": 100, "Width": 100, "Height": 10, "Material": "Plywood" }, { "object_name": "Yellow_Forklift_1", "X": 50, "Y": 0.0, "Z": -50, "Length": 200, "Width": 100, "Height": 250, "Material": "Plastic_ABS" }, { "object_name": "Heavy_Duty_Forklift_2", "X": 150, "Y": 0.0, "Z": -50, "Length": 200, "Width": 100, "Height": 250, "Material": "Steel_Stainless" } ] }'''

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.example.airoomgenerator/omni/example/airoomgenerator/materials.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. MaterialPresets = { "Leather_Brown": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Textiles/Leather_Brown.mdl', "Leather_Pumpkin_01": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Textiles/Leather_Pumpkin.mdl', "Leather_Brown_02": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Textiles/Leather_Brown.mdl', "Leather_Black_01": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Textiles/Leather_Black.mdl', "Aluminum_cast": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Metals/Aluminum_Cast.mdl', "Birch": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Wood/Birch.mdl', "Beadboard": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Wood/Beadboard.mdl', "Cardboard": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Wall_Board/Cardboard.mdl', "Cloth_Black": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Textiles/Cloth_Black.mdl', "Cloth_Gray": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Textiles/Cloth_Gray.mdl', "Concrete_Polished": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Masonry/Concrete_Polished.mdl', "Glazed_Glass": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Glass/Glazed_Glass.mdl', "CorrugatedMetal": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Metals/CorrugatedMetal.mdl', "Cork": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Wood/Cork.mdl', "Linen_Beige": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Textiles/Linen_Beige.mdl', "Linen_Blue": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Textiles/Linen_Blue.mdl', "Linen_White": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Textiles/Linen_White.mdl', "Mahogany": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Wood/Mahogany.mdl', "MDF": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Wall_Board/MDF.mdl', "Oak": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Wood/Oak.mdl', "Plastic_ABS": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Plastics/Plastic_ABS.mdl', "Steel_Carbon": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Metals/Steel_Carbon.mdl', "Steel_Stainless": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Metals/Steel_Stainless.mdl', "Veneer_OU_Walnut": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Plastics/Veneer_OU_Walnut.mdl', "Veneer_UX_Walnut_Cherry": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Plastics/Veneer_UX_Walnut_Cherry.mdl', "Veneer_Z5_Maple": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Plastics/Veneer_Z5_Maple.mdl', "Plywood": 'http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Wood/Plywood.mdl', "Concrete_Rough_Dirty": 'http://omniverse-content-production.s3.us-west-2.amazonaws.com/Materials/vMaterials_2/Concrete/Concrete_Rough.mdl' }

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.example.airoomgenerator/omni/example/airoomgenerator/utils.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.kit.commands from pxr import Gf, Sdf, UsdGeom from .materials import * import carb def CreateCubeFromCurve(curve_path: str, area_name: str = ""): ctx = omni.usd.get_context() stage = ctx.get_stage() min_coords, max_coords = get_coords_from_bbox(curve_path) x,y,z = get_bounding_box_dimensions(curve_path) xForm_scale = Gf.Vec3d(x, 1, z) cube_scale = Gf.Vec3d(0.01, 0.01, 0.01) prim = stage.GetPrimAtPath(curve_path) origin = prim.GetAttribute('xformOp:translate').Get() if prim.GetTypeName() == "BasisCurves": origin = Gf.Vec3d(min_coords[0]+x/2, 0, min_coords[2]+z/2) area_path = '/World/Layout/Area' if len(area_name) != 0: area_path = '/World/Layout/' + area_name.replace(" ", "_") new_area_path = omni.usd.get_stage_next_free_path(stage, area_path, False) new_cube_xForm_path = new_area_path + "/" + "Floor" new_cube_path = new_cube_xForm_path + "/" + "Cube" # Create xForm to hold all items item_container = create_prim(new_area_path) set_transformTRS_attrs(item_container, translate=origin) # Create Scale Xform for floor xform = create_prim(new_cube_xForm_path) set_transformTRS_attrs(xform, scale=xForm_scale) # Create Floor Cube omni.kit.commands.execute('CreateMeshPrimWithDefaultXform', prim_type='Cube', prim_path=new_cube_path, select_new_prim=True ) cube = stage.GetPrimAtPath(new_cube_path) set_transformTRS_attrs(cube, scale=cube_scale) cube.CreateAttribute("primvar:area_name", Sdf.ValueTypeNames.String, custom=True).Set(area_name) omni.kit.commands.execute('DeletePrims', paths=[curve_path], destructive=False) apply_material_to_prim('Concrete_Rough_Dirty', new_area_path) return new_area_path def apply_material_to_prim(material_name: str, prim_path: str): ctx = omni.usd.get_context() stage = ctx.get_stage() looks_path = '/World/Looks/' mat_path = looks_path + material_name mat_prim = stage.GetPrimAtPath(mat_path) if MaterialPresets.get(material_name, None) is not None: if not mat_prim.IsValid(): omni.kit.commands.execute('CreateMdlMaterialPrimCommand', mtl_url=MaterialPresets[material_name], mtl_name=material_name, mtl_path=mat_path) omni.kit.commands.execute('BindMaterialCommand', prim_path=prim_path, material_path=mat_path) def create_prim(prim_path, prim_type='Xform'): ctx = omni.usd.get_context() stage = ctx.get_stage() prim = stage.DefinePrim(prim_path) if prim_type == 'Xform': xform = UsdGeom.Xform.Define(stage, prim_path) else: xform = UsdGeom.Cube.Define(stage, prim_path) create_transformOps_for_xform(xform) return prim def create_transformOps_for_xform(xform): xform.AddTranslateOp() xform.AddRotateXYZOp() xform.AddScaleOp() def set_transformTRS_attrs(prim, translate: Gf.Vec3d = Gf.Vec3d(0,0,0), rotate: Gf.Vec3d=Gf.Vec3d(0,0,0), scale: Gf.Vec3d=Gf.Vec3d(1,1,1)): prim.GetAttribute('xformOp:translate').Set(translate) prim.GetAttribute('xformOp:rotateXYZ').Set(rotate) prim.GetAttribute('xformOp:scale').Set(scale) def get_bounding_box_dimensions(prim_path: str): min_coords, max_coords = get_coords_from_bbox(prim_path) length = max_coords[0] - min_coords[0] width = max_coords[1] - min_coords[1] height = max_coords[2] - min_coords[2] return length, width, height def get_coords_from_bbox(prim_path: str): ctx = omni.usd.get_context() bbox = ctx.compute_path_world_bounding_box(prim_path) min_coords, max_coords = bbox return min_coords, max_coords def scale_object_if_needed(prim_path): stage = omni.usd.get_context().get_stage() length, width, height = get_bounding_box_dimensions(prim_path) largest_dimension = max(length, width, height) if largest_dimension < 10: prim = stage.GetPrimAtPath(prim_path) # HACK: All Get Attribute Calls need to check if the attribute exists and add it if it doesn't if prim.IsValid(): scale_attr = prim.GetAttribute('xformOp:scale') if scale_attr.IsValid(): current_scale = scale_attr.Get() new_scale = (current_scale[0] * 100, current_scale[1] * 100, current_scale[2] * 100) scale_attr.Set(new_scale) carb.log_info(f"Scaled object by 100 times: {prim_path}") else: carb.log_info(f"Scale attribute not found for prim at path: {prim_path}") else: carb.log_info(f"Invalid prim at path: {prim_path}") else: carb.log_info(f"No scaling needed for object: {prim_path}")

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.example.airoomgenerator/omni/example/airoomgenerator/item_generator.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #hotkey # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pxr import Usd, Sdf, Gf from .utils import scale_object_if_needed, apply_material_to_prim, create_prim, set_transformTRS_attrs def place_deepsearch_results(gpt_results, query_result, root_prim_path): index = 0 for item in query_result: item_name = item[0] item_path = item[1] # Define Prim prim_parent_path = root_prim_path + item_name.replace(" ", "_") prim_path = prim_parent_path + "/" + item_name.replace(" ", "_") parent_prim = create_prim(prim_parent_path) next_prim = create_prim(prim_path) # Add reference to USD Asset references: Usd.references = next_prim.GetReferences() # TODO: The query results should returnt he full path of the prim references.AddReference( assetPath="omniverse://ov-simready" + item_path) # Add reference for future search refinement config = next_prim.CreateAttribute("DeepSearch:Query", Sdf.ValueTypeNames.String) config.Set(item_name) # HACK: All "GetAttribute" calls should need to check if the attribute exists # translate prim next_object = gpt_results[index] index = index + 1 x = next_object['X'] y = next_object['Y'] z = next_object['Z'] set_transformTRS_attrs(parent_prim, Gf.Vec3d(x,y,z), Gf.Vec3d(0,-90,-90), Gf.Vec3d(1.0,1.0,1.0)) scale_object_if_needed(prim_parent_path) def place_greyboxes(gpt_results, root_prim_path): index = 0 for item in gpt_results: # Define Prim prim_parent_path = root_prim_path + item['object_name'].replace(" ", "_") prim_path = prim_parent_path + "/" + item['object_name'].replace(" ", "_") # Define Dimensions and material length = item['Length']/100 width = item['Width']/100 height = item['Height']/100 x = item['X'] y = item['Y']+height*100*.5 #shift bottom of object to y=0 z = item['Z'] material = item['Material'] # Create Prim parent_prim = create_prim(prim_parent_path) set_transformTRS_attrs(parent_prim) prim = create_prim(prim_path, 'Cube') set_transformTRS_attrs(prim, translate=Gf.Vec3d(x,y,z), scale=Gf.Vec3d(length, height, width)) prim.GetAttribute('extent').Set([(-50.0, -50.0, -50.0), (50.0, 50.0, 50.0)]) prim.GetAttribute('size').Set(100) index = index + 1 # Add Attribute and Material attr = prim.CreateAttribute("object_name", Sdf.ValueTypeNames.String) attr.Set(item['object_name']) apply_material_to_prim(material, prim_path)

Python

NVIDIA-Omniverse/kit-extension-sample-airoomgenerator/exts/omni.example.airoomgenerator/omni/example/airoomgenerator/window.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import omni.ui as ui import omni.usd import carb import asyncio import omni.kit.commands from omni.kit.window.popup_dialog.form_dialog import FormDialog from .utils import CreateCubeFromCurve from .style import gen_ai_style, guide from .chatgpt_apiconnect import call_Generate from .priminfo import PrimInfo from pxr import Sdf from .widgets import ProgressBar class GenAIWindow(ui.Window): def __init__(self, title: str, **kwargs) -> None: super().__init__(title, **kwargs) # Models self._path_model = ui.SimpleStringModel() self._prompt_model = ui.SimpleStringModel("generate warehouse objects") self._area_name_model = ui.SimpleStringModel() self._use_deepsearch = ui.SimpleBoolModel() self._use_chatgpt = ui.SimpleBoolModel() self._areas = [] self.response_log = None self.current_index = -1 self.current_area = None self._combo_changed_sub = None self.frame.set_build_fn(self._build_fn) def _build_fn(self): with self.frame: with ui.ScrollingFrame(): with ui.VStack(style=gen_ai_style): with ui.HStack(height=0): ui.Label("Content Generatation with ChatGPT", style={"font_size": 18}) ui.Button(name="properties", tooltip="Configure API Key and Nucleus Path", width=30, height=30, clicked_fn=lambda: self._open_settings()) with ui.CollapsableFrame("Getting Started Instructions", height=0, collapsed=True): ui.Label(guide, word_wrap=True) ui.Line() with ui.HStack(height=0): ui.Label("Area Name", width=ui.Percent(30)) ui.StringField(model=self._area_name_model) ui.Button(name="create", width=30, height=30, clicked_fn=lambda: self._create_new_area(self.get_area_name())) with ui.HStack(height=0): ui.Label("Current Room", width=ui.Percent(30)) self._build_combo_box() ui.Line() with ui.HStack(height=ui.Percent(50)): ui.Label("Prompt", width=0) ui.StringField(model=self._prompt_model, multiline=True) ui.Line() self._build_ai_section() def _save_settings(self, dialog): values = dialog.get_values() carb.log_info(values) settings = carb.settings.get_settings() settings.set_string("/persistent/exts/omni.example.airoomgenerator/APIKey", values["APIKey"]) settings.set_string("/persistent/exts/omni.example.airoomgenerator/deepsearch_nucleus_path", values["deepsearch_nucleus_path"]) settings.set_string("/persistent/exts/omni.example.airoomgenerator/path_filter", values["path_filter"]) dialog.hide() def _open_settings(self): settings = carb.settings.get_settings() apikey_value = settings.get_as_string("/persistent/exts/omni.example.airoomgenerator/APIKey") nucleus_path = settings.get_as_string("/persistent/exts/omni.example.airoomgenerator/deepsearch_nucleus_path") path_filter = settings.get_as_string("/persistent/exts/omni.example.airoomgenerator/path_filter") if apikey_value == "": apikey_value = "Enter API Key Here" if nucleus_path == "": nucleus_path = "(ENTERPRISE ONLY) Enter Nucleus Path Here" if path_filter == "": path_filter = "" field_defs = [ FormDialog.FieldDef("APIKey", "API Key: ", ui.StringField, apikey_value), FormDialog.FieldDef("deepsearch_nucleus_path", "Nucleus Path: ", ui.StringField, nucleus_path), FormDialog.FieldDef("path_filter", "Path Filter: ", ui.StringField, path_filter) ] dialog = FormDialog( title="Settings", message="Your Settings: ", field_defs = field_defs, ok_handler=lambda dialog: self._save_settings(dialog)) dialog.show() def _build_ai_section(self): with ui.HStack(height=0): ui.Spacer() ui.Label("Use ChatGPT: ") ui.CheckBox(model=self._use_chatgpt) ui.Label("Use Deepsearch: ", tooltip="ENTERPRISE USERS ONLY") ui.CheckBox(model=self._use_deepsearch) ui.Spacer() with ui.HStack(height=0): ui.Spacer(width=ui.Percent(10)) ui.Button("Generate", height=40, clicked_fn=lambda: self._generate()) ui.Spacer(width=ui.Percent(10)) self.progress = ProgressBar() with ui.CollapsableFrame("ChatGPT Response / Log", height=0, collapsed=True): self.response_log = ui.Label("", word_wrap=True) def _build_combo_box(self): self.combo_model = ui.ComboBox(self.current_index, *[str(x) for x in self._areas] ).model def combo_changed(item_model, item): index_value_model = item_model.get_item_value_model(item) self.current_area = self._areas[index_value_model.as_int] self.current_index = index_value_model.as_int self.rebuild_frame() self._combo_changed_sub = self.combo_model.subscribe_item_changed_fn(combo_changed) def _create_new_area(self, area_name: str): if area_name == "": carb.log_warn("No area name provided") return new_area_name = CreateCubeFromCurve(self.get_prim_path(), area_name) self._areas.append(new_area_name) self.current_index = len(self._areas) - 1 index_value_model = self.combo_model.get_item_value_model() index_value_model.set_value(self.current_index) def rebuild_frame(self): # we do want to update the area name and possibly last prompt? area_name = self.current_area.split("/World/Layout/") self._area_name_model.set_value(area_name[-1].replace("_", " ")) attr_prompt = self.get_prim().GetAttribute('genai:prompt') if attr_prompt.IsValid(): self._prompt_model.set_value(attr_prompt.Get()) else: self._prompt_model.set_value("") self.frame.rebuild() def _generate(self): prim = self.get_prim() attr = prim.GetAttribute('genai:prompt') if not attr.IsValid(): attr = prim.CreateAttribute('genai:prompt', Sdf.ValueTypeNames.String) attr.Set(self.get_prompt()) items_path = self.current_area + "/items" ctx = omni.usd.get_context() stage = ctx.get_stage() if stage.GetPrimAtPath(items_path).IsValid(): omni.kit.commands.execute('DeletePrims', paths=[items_path], destructive=False) # asyncio.ensure_future(self.progress.fill_bar(0,100)) run_loop = asyncio.get_event_loop() run_loop.create_task(call_Generate(self.get_prim_info(), self.get_prompt(), self._use_chatgpt.as_bool, self._use_deepsearch.as_bool, self.response_log, self.progress )) # Returns a PrimInfo object containing the Length, Width, Origin and Area Name def get_prim_info(self) -> PrimInfo: prim = self.get_prim() prim_info = None if prim.IsValid(): prim_info = PrimInfo(prim, self.current_area) return prim_info # # Get the prim path specified def get_prim_path(self): ctx = omni.usd.get_context() selection = ctx.get_selection().get_selected_prim_paths() if len(selection) > 0: return str(selection[0]) carb.log_warn("No Prim Selected") return "" # Get the area name specified def get_area_name(self): if self._area_name_model == "": carb.log_warn("No Area Name Provided") return self._area_name_model.as_string # Get the prompt specified def get_prompt(self): if self._prompt_model == "": carb.log_warn("No Prompt Provided") return self._prompt_model.as_string # Get the prim based on the Prim Path def get_prim(self): ctx = omni.usd.get_context() stage = ctx.get_stage() prim = stage.GetPrimAtPath(self.current_area) if prim.IsValid() is None: carb.log_warn("No valid prim in the scene") return prim def destroy(self): super().destroy() self._combo_changed_sub = None self._path_model = None self._prompt_model = None self._area_name_model = None self._use_deepsearch = None self._use_chatgpt = None

Python

NVIDIA-Omniverse/orbit/tools/tests_to_skip.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause # The following tests are skipped by run_tests.py TESTS_TO_SKIP = [ # orbit "test_argparser_launch.py", # app.close issue "test_env_var_launch.py", # app.close issue "test_kwarg_launch.py", # app.close issue "test_differential_ik.py", # Failing # orbit_tasks "test_data_collector.py", # Failing "test_record_video.py", # Failing "test_rsl_rl_wrapper.py", # Timing out (10 minutes) "test_sb3_wrapper.py", # Timing out (10 minutes) ]

Python

NVIDIA-Omniverse/orbit/tools/run_all_tests.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """A runner script for all the tests within source directory. .. code-block:: bash ./orbit.sh -p tools/run_all_tests.py # for dry run ./orbit.sh -p tools/run_all_tests.py --discover_only # for quiet run ./orbit.sh -p tools/run_all_tests.py --quiet # for increasing timeout (default is 600 seconds) ./orbit.sh -p tools/run_all_tests.py --timeout 1000 """ from __future__ import annotations import argparse import logging import os import subprocess import sys import time from datetime import datetime from pathlib import Path from prettytable import PrettyTable # Tests to skip from tests_to_skip import TESTS_TO_SKIP ORBIT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) """Path to the root directory of Orbit repository.""" def parse_args() -> argparse.Namespace: """Parse command line arguments.""" parser = argparse.ArgumentParser(description="Run all tests under current directory.") # add arguments parser.add_argument( "--skip_tests", default="", help="Space separated list of tests to skip in addition to those in tests_to_skip.py.", type=str, nargs="*", ) # configure default test directory (source directory) default_test_dir = os.path.join(ORBIT_PATH, "source") parser.add_argument( "--test_dir", type=str, default=default_test_dir, help="Path to the directory containing the tests." ) # configure default logging path based on time stamp log_file_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + ".log" default_log_path = os.path.join(ORBIT_PATH, "logs", "test_results", log_file_name) parser.add_argument( "--log_path", type=str, default=default_log_path, help="Path to the log file to store the results in." ) parser.add_argument("--discover_only", action="store_true", help="Only discover and print tests, don't run them.") parser.add_argument("--quiet", action="store_true", help="Don't print to console, only log to file.") parser.add_argument("--timeout", type=int, default=600, help="Timeout for each test in seconds.") # parse arguments args = parser.parse_args() return args def test_all( test_dir: str, tests_to_skip: list[str], log_path: str, timeout: float = 600.0, discover_only: bool = False, quiet: bool = False, ) -> bool: """Run all tests under the given directory. Args: test_dir: Path to the directory containing the tests. tests_to_skip: List of tests to skip. log_path: Path to the log file to store the results in. timeout: Timeout for each test in seconds. Defaults to 600 seconds (10 minutes). discover_only: If True, only discover and print the tests without running them. Defaults to False. quiet: If False, print the output of the tests to the terminal console (in addition to the log file). Defaults to False. Returns: True if all un-skipped tests pass or `discover_only` is True. Otherwise, False. Raises: ValueError: If any test to skip is not found under the given `test_dir`. """ # Create the log directory if it doesn't exist os.makedirs(os.path.dirname(log_path), exist_ok=True) # Add file handler to log to file logging_handlers = [logging.FileHandler(log_path)] # We also want to print to console if not quiet: logging_handlers.append(logging.StreamHandler()) # Set up logger logging.basicConfig(level=logging.INFO, format="%(message)s", handlers=logging_handlers) # Discover all tests under current directory all_test_paths = [str(path) for path in Path(test_dir).resolve().rglob("*test_*.py")] skipped_test_paths = [] test_paths = [] # Check that all tests to skip are actually in the tests for test_to_skip in tests_to_skip: for test_path in all_test_paths: if test_to_skip in test_path: break else: raise ValueError(f"Test to skip '{test_to_skip}' not found in tests.") # Remove tests to skip from the list of tests to run if len(tests_to_skip) != 0: for test_path in all_test_paths: if any([test_to_skip in test_path for test_to_skip in tests_to_skip]): skipped_test_paths.append(test_path) else: test_paths.append(test_path) else: test_paths = all_test_paths # Sort test paths so they're always in the same order all_test_paths.sort() test_paths.sort() skipped_test_paths.sort() # Print tests to be run logging.info("\n" + "=" * 60 + "\n") logging.info(f"The following {len(all_test_paths)} tests were found:") for i, test_path in enumerate(all_test_paths): logging.info(f"{i + 1:02d}: {test_path}") logging.info("\n" + "=" * 60 + "\n") logging.info(f"The following {len(skipped_test_paths)} tests are marked to be skipped:") for i, test_path in enumerate(skipped_test_paths): logging.info(f"{i + 1:02d}: {test_path}") logging.info("\n" + "=" * 60 + "\n") # Exit if only discovering tests if discover_only: return True results = {} # Run each script and store results for test_path in test_paths: results[test_path] = {} before = time.time() logging.info("\n" + "-" * 60 + "\n") logging.info(f"[INFO] Running '{test_path}'\n") try: completed_process = subprocess.run( [sys.executable, test_path], check=True, capture_output=True, timeout=timeout ) except subprocess.TimeoutExpired as e: logging.error(f"Timeout occurred: {e}") result = "TIMEDOUT" stdout = e.stdout stderr = e.stderr except subprocess.CalledProcessError as e: # When check=True is passed to subprocess.run() above, CalledProcessError is raised if the process returns a # non-zero exit code. The caveat is returncode is not correctly updated in this case, so we simply # catch the exception and set this test as FAILED result = "FAILED" stdout = e.stdout stderr = e.stderr except Exception as e: logging.error(f"Unexpected exception {e}. Please report this issue on the repository.") result = "FAILED" stdout = e.stdout stderr = e.stderr else: # Should only get here if the process ran successfully, e.g. no exceptions were raised # but we still check the returncode just in case result = "PASSED" if completed_process.returncode == 0 else "FAILED" stdout = completed_process.stdout stderr = completed_process.stderr after = time.time() time_elapsed = after - before # Decode stdout and stderr and write to file and print to console if desired stdout_str = stdout.decode("utf-8") if stdout is not None else "" stderr_str = stderr.decode("utf-8") if stderr is not None else "" # Write to log file logging.info(stdout_str) logging.info(stderr_str) logging.info(f"[INFO] Time elapsed: {time_elapsed:.2f} s") logging.info(f"[INFO] Result '{test_path}': {result}") # Collect results results[test_path]["time_elapsed"] = time_elapsed results[test_path]["result"] = result # Calculate the number and percentage of passing tests num_tests = len(all_test_paths) num_passing = len([test_path for test_path in test_paths if results[test_path]["result"] == "PASSED"]) num_failing = len([test_path for test_path in test_paths if results[test_path]["result"] == "FAILED"]) num_timing_out = len([test_path for test_path in test_paths if results[test_path]["result"] == "TIMEDOUT"]) num_skipped = len(skipped_test_paths) if num_tests == 0: passing_percentage = 100 else: passing_percentage = (num_passing + num_skipped) / num_tests * 100 # Print summaries of test results summary_str = "\n\n" summary_str += "===================\n" summary_str += "Test Result Summary\n" summary_str += "===================\n" summary_str += f"Total: {num_tests}\n" summary_str += f"Passing: {num_passing}\n" summary_str += f"Failing: {num_failing}\n" summary_str += f"Skipped: {num_skipped}\n" summary_str += f"Timing Out: {num_timing_out}\n" summary_str += f"Passing Percentage: {passing_percentage:.2f}%\n" # Print time elapsed in hours, minutes, seconds total_time = sum([results[test_path]["time_elapsed"] for test_path in test_paths]) summary_str += f"Total Time Elapsed: {total_time // 3600}h" summary_str += f"{total_time // 60 % 60}m" summary_str += f"{total_time % 60:.2f}s" summary_str += "\n\n=======================\n" summary_str += "Per Test Result Summary\n" summary_str += "=======================\n" # Construct table of results per test per_test_result_table = PrettyTable(field_names=["Test Path", "Result", "Time (s)"]) per_test_result_table.align["Test Path"] = "l" per_test_result_table.align["Time (s)"] = "r" for test_path in test_paths: per_test_result_table.add_row( [test_path, results[test_path]["result"], f"{results[test_path]['time_elapsed']:0.2f}"] ) for test_path in skipped_test_paths: per_test_result_table.add_row([test_path, "SKIPPED", "N/A"]) summary_str += per_test_result_table.get_string() # Print summary to console and log file logging.info(summary_str) # Only count failing and timing out tests towards failure return num_failing + num_timing_out == 0 if __name__ == "__main__": # parse command line arguments args = parse_args() # add tests to skip to the list of tests to skip tests_to_skip = TESTS_TO_SKIP tests_to_skip += args.skip_tests # run all tests test_success = test_all( test_dir=args.test_dir, tests_to_skip=tests_to_skip, log_path=args.log_path, timeout=args.timeout, discover_only=args.discover_only, quiet=args.quiet, ) # update exit status based on all tests passing or not if not test_success: exit(1)

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/setup.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """Installation script for the 'omni.isaac.orbit_tasks' python package.""" import itertools import os import toml from setuptools import setup # Obtain the extension data from the extension.toml file EXTENSION_PATH = os.path.dirname(os.path.realpath(__file__)) # Read the extension.toml file EXTENSION_TOML_DATA = toml.load(os.path.join(EXTENSION_PATH, "config", "extension.toml")) # Minimum dependencies required prior to installation INSTALL_REQUIRES = [ # generic "numpy", "torch==2.0.1", "torchvision>=0.14.1", # ensure compatibility with torch 1.13.1 "protobuf>=3.20.2", # data collection "h5py", # basic logger "tensorboard", # video recording "moviepy", ] # Extra dependencies for RL agents EXTRAS_REQUIRE = { "sb3": ["stable-baselines3>=2.0"], "skrl": ["skrl>=1.1.0"], "rl_games": ["rl-games==1.6.1", "gym"], # rl-games still needs gym :( "rsl_rl": ["rsl_rl@git+https://github.com/leggedrobotics/rsl_rl.git"], "robomimic": ["robomimic@git+https://github.com/ARISE-Initiative/robomimic.git"], } # cumulation of all extra-requires EXTRAS_REQUIRE["all"] = list(itertools.chain.from_iterable(EXTRAS_REQUIRE.values())) # Installation operation setup( name="omni-isaac-orbit_tasks", author="ORBIT Project Developers", maintainer="Mayank Mittal", maintainer_email="mittalma@ethz.ch", url=EXTENSION_TOML_DATA["package"]["repository"], version=EXTENSION_TOML_DATA["package"]["version"], description=EXTENSION_TOML_DATA["package"]["description"], keywords=EXTENSION_TOML_DATA["package"]["keywords"], include_package_data=True, python_requires=">=3.10", install_requires=INSTALL_REQUIRES, extras_require=EXTRAS_REQUIRE, packages=["omni.isaac.orbit_tasks"], classifiers=[ "Natural Language :: English", "Programming Language :: Python :: 3.10", "Isaac Sim :: 2023.1.0-hotfix.1", "Isaac Sim :: 2023.1.1", ], zip_safe=False, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/test/test_environments.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations """Launch Isaac Sim Simulator first.""" from omni.isaac.orbit.app import AppLauncher, run_tests # launch the simulator app_launcher = AppLauncher(headless=True) simulation_app = app_launcher.app """Rest everything follows.""" import gymnasium as gym import torch import unittest import omni.usd from omni.isaac.orbit.envs import RLTaskEnv, RLTaskEnvCfg import omni.isaac.orbit_tasks # noqa: F401 from omni.isaac.orbit_tasks.utils.parse_cfg import parse_env_cfg class TestEnvironments(unittest.TestCase): """Test cases for all registered environments.""" @classmethod def setUpClass(cls): # acquire all Isaac environments names cls.registered_tasks = list() for task_spec in gym.registry.values(): if "Isaac" in task_spec.id: cls.registered_tasks.append(task_spec.id) # sort environments by name cls.registered_tasks.sort() # print all existing task names print(">>> All registered environments:", cls.registered_tasks) """ Test fixtures. """ def test_multiple_instances_gpu(self): """Run all environments with multiple instances and check environments return valid signals.""" # common parameters num_envs = 32 use_gpu = True # iterate over all registered environments for task_name in self.registered_tasks: print(f">>> Running test for environment: {task_name}") # check environment self._check_random_actions(task_name, use_gpu, num_envs, num_steps=100) # close the environment print(f">>> Closing environment: {task_name}") print("-" * 80) def test_single_instance_gpu(self): """Run all environments with single instance and check environments return valid signals.""" # common parameters num_envs = 1 use_gpu = True # iterate over all registered environments for task_name in self.registered_tasks: print(f">>> Running test for environment: {task_name}") # check environment self._check_random_actions(task_name, use_gpu, num_envs, num_steps=100) # close the environment print(f">>> Closing environment: {task_name}") print("-" * 80) """ Helper functions. """ def _check_random_actions(self, task_name: str, use_gpu: bool, num_envs: int, num_steps: int = 1000): """Run random actions and check environments return valid signals.""" # create a new stage omni.usd.get_context().new_stage() # parse configuration env_cfg: RLTaskEnvCfg = parse_env_cfg(task_name, use_gpu=use_gpu, num_envs=num_envs) # create environment env: RLTaskEnv = gym.make(task_name, cfg=env_cfg) # reset environment obs, _ = env.reset() # check signal self.assertTrue(self._check_valid_tensor(obs)) # simulate environment for num_steps steps with torch.inference_mode(): for _ in range(num_steps): # sample actions from -1 to 1 actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1 # apply actions transition = env.step(actions) # check signals for data in transition: self.assertTrue(self._check_valid_tensor(data), msg=f"Invalid data: {data}") # close the environment env.close() @staticmethod def _check_valid_tensor(data: torch.Tensor | dict) -> bool: """Checks if given data does not have corrupted values. Args: data: Data buffer. Returns: True if the data is valid. """ if isinstance(data, torch.Tensor): return not torch.any(torch.isnan(data)) elif isinstance(data, dict): valid_tensor = True for value in data.values(): if isinstance(value, dict): valid_tensor &= TestEnvironments._check_valid_tensor(value) elif isinstance(value, torch.Tensor): valid_tensor &= not torch.any(torch.isnan(value)) return valid_tensor else: raise ValueError(f"Input data of invalid type: {type(data)}.") if __name__ == "__main__": run_tests()

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/test/test_data_collector.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations """Launch Isaac Sim Simulator first.""" from omni.isaac.orbit.app import AppLauncher, run_tests # launch the simulator app_launcher = AppLauncher(headless=True) simulation_app = app_launcher.app """Rest everything follows.""" import os import torch import unittest from omni.isaac.orbit_tasks.utils.data_collector import RobomimicDataCollector class TestRobomimicDataCollector(unittest.TestCase): """Test dataset flushing behavior of robomimic data collector.""" def test_basic_flushing(self): """Adds random data into the collector and checks saving of the data.""" # name of the environment (needed by robomimic) task_name = "My-Task-v0" # specify directory for logging experiments test_dir = os.path.dirname(os.path.abspath(__file__)) log_dir = os.path.join(test_dir, "output", "demos") # name of the file to save data filename = "hdf_dataset.hdf5" # number of episodes to collect num_demos = 10 # number of environments to simulate num_envs = 4 # create data-collector collector_interface = RobomimicDataCollector(task_name, log_dir, filename, num_demos) # reset the collector collector_interface.reset() while not collector_interface.is_stopped(): # generate random data to store # -- obs obs = {"joint_pos": torch.randn(num_envs, 7), "joint_vel": torch.randn(num_envs, 7)} # -- actions actions = torch.randn(num_envs, 7) # -- next obs next_obs = {"joint_pos": torch.randn(num_envs, 7), "joint_vel": torch.randn(num_envs, 7)} # -- rewards rewards = torch.randn(num_envs) # -- dones dones = torch.rand(num_envs) > 0.5 # store signals # -- obs for key, value in obs.items(): collector_interface.add(f"obs/{key}", value) # -- actions collector_interface.add("actions", actions) # -- next_obs for key, value in next_obs.items(): collector_interface.add(f"next_obs/{key}", value.cpu().numpy()) # -- rewards collector_interface.add("rewards", rewards) # -- dones collector_interface.add("dones", dones) # flush data from collector for successful environments # note: in this case we flush all the time reset_env_ids = dones.nonzero(as_tuple=False).squeeze(-1) collector_interface.flush(reset_env_ids) # close collector collector_interface.close() # TODO: Add inspection of the saved dataset as part of the test. if __name__ == "__main__": run_tests()

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/test/test_record_video.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations """Launch Isaac Sim Simulator first.""" from omni.isaac.orbit.app import AppLauncher, run_tests # launch the simulator app_launcher = AppLauncher(headless=True, offscreen_render=True) simulation_app = app_launcher.app """Rest everything follows.""" import gymnasium as gym import os import torch import unittest import omni.usd from omni.isaac.orbit.envs import RLTaskEnv, RLTaskEnvCfg import omni.isaac.orbit_tasks # noqa: F401 from omni.isaac.orbit_tasks.utils import parse_env_cfg class TestRecordVideoWrapper(unittest.TestCase): """Test recording videos using the RecordVideo wrapper.""" @classmethod def setUpClass(cls): # acquire all Isaac environments names cls.registered_tasks = list() for task_spec in gym.registry.values(): if "Isaac" in task_spec.id: cls.registered_tasks.append(task_spec.id) # sort environments by name cls.registered_tasks.sort() # print all existing task names print(">>> All registered environments:", cls.registered_tasks) # directory to save videos cls.videos_dir = os.path.join(os.path.dirname(__file__), "output", "videos") def setUp(self) -> None: # common parameters self.num_envs = 16 self.use_gpu = True # video parameters self.step_trigger = lambda step: step % 225 == 0 self.video_length = 200 def test_record_video(self): """Run random actions agent with recording of videos.""" for task_name in self.registered_tasks: print(f">>> Running test for environment: {task_name}") # create a new stage omni.usd.get_context().new_stage() # parse configuration env_cfg: RLTaskEnvCfg = parse_env_cfg(task_name, use_gpu=self.use_gpu, num_envs=self.num_envs) # create environment env: RLTaskEnv = gym.make(task_name, cfg=env_cfg, render_mode="rgb_array") # directory to save videos videos_dir = os.path.join(self.videos_dir, task_name) # wrap environment to record videos env = gym.wrappers.RecordVideo( env, videos_dir, step_trigger=self.step_trigger, video_length=self.video_length, disable_logger=True ) # reset environment env.reset() # simulate environment with torch.inference_mode(): for _ in range(500): # compute zero actions actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1 # apply actions _ = env.step(actions) # close the simulator env.close() if __name__ == "__main__": run_tests()

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/test/wrappers/test_rsl_rl_wrapper.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations """Launch Isaac Sim Simulator first.""" from omni.isaac.orbit.app import AppLauncher, run_tests # launch the simulator app_launcher = AppLauncher(headless=True) simulation_app = app_launcher.app """Rest everything follows.""" import gymnasium as gym import torch import unittest import omni.usd from omni.isaac.orbit.envs import RLTaskEnvCfg import omni.isaac.orbit_tasks # noqa: F401 from omni.isaac.orbit_tasks.utils.parse_cfg import parse_env_cfg from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import RslRlVecEnvWrapper class TestRslRlVecEnvWrapper(unittest.TestCase): """Test that RSL-RL VecEnv wrapper works as expected.""" @classmethod def setUpClass(cls): # acquire all Isaac environments names cls.registered_tasks = list() for task_spec in gym.registry.values(): if "Isaac" in task_spec.id: cls.registered_tasks.append(task_spec.id) # sort environments by name cls.registered_tasks.sort() # only pick the first three environments to test cls.registered_tasks = cls.registered_tasks[:3] # print all existing task names print(">>> All registered environments:", cls.registered_tasks) def setUp(self) -> None: # common parameters self.num_envs = 64 self.use_gpu = True def test_random_actions(self): """Run random actions and check environments return valid signals.""" for task_name in self.registered_tasks: print(f">>> Running test for environment: {task_name}") # create a new stage omni.usd.get_context().new_stage() # parse configuration env_cfg: RLTaskEnvCfg = parse_env_cfg(task_name, use_gpu=self.use_gpu, num_envs=self.num_envs) # create environment env = gym.make(task_name, cfg=env_cfg) # wrap environment env = RslRlVecEnvWrapper(env) # reset environment obs, extras = env.reset() # check signal self.assertTrue(self._check_valid_tensor(obs)) self.assertTrue(self._check_valid_tensor(extras)) # simulate environment for 1000 steps with torch.inference_mode(): for _ in range(1000): # sample actions from -1 to 1 actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1 # apply actions transition = env.step(actions) # check signals for data in transition: self.assertTrue(self._check_valid_tensor(data), msg=f"Invalid data: {data}") # close the environment print(f">>> Closing environment: {task_name}") env.close() def test_no_time_outs(self): """Check that environments with finite horizon do not send time-out signals.""" for task_name in self.registered_tasks[0:5]: print(f">>> Running test for environment: {task_name}") # create a new stage omni.usd.get_context().new_stage() # parse configuration env_cfg: RLTaskEnvCfg = parse_env_cfg(task_name, use_gpu=self.use_gpu, num_envs=self.num_envs) # change to finite horizon env_cfg.is_finite_horizon = True # create environment env = gym.make(task_name, cfg=env_cfg) # wrap environment env = RslRlVecEnvWrapper(env) # reset environment _, extras = env.reset() # check signal self.assertNotIn("time_outs", extras, msg="Time-out signal found in finite horizon environment.") # simulate environment for 10 steps with torch.inference_mode(): for _ in range(10): # sample actions from -1 to 1 actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1 # apply actions extras = env.step(actions)[-1] # check signals self.assertNotIn("time_outs", extras, msg="Time-out signal found in finite horizon environment.") # close the environment print(f">>> Closing environment: {task_name}") env.close() """ Helper functions. """ @staticmethod def _check_valid_tensor(data: torch.Tensor | dict) -> bool: """Checks if given data does not have corrupted values. Args: data: Data buffer. Returns: True if the data is valid. """ if isinstance(data, torch.Tensor): return not torch.any(torch.isnan(data)) elif isinstance(data, dict): valid_tensor = True for value in data.values(): if isinstance(value, dict): valid_tensor &= TestRslRlVecEnvWrapper._check_valid_tensor(value) elif isinstance(value, torch.Tensor): valid_tensor &= not torch.any(torch.isnan(value)) return valid_tensor else: raise ValueError(f"Input data of invalid type: {type(data)}.") if __name__ == "__main__": run_tests()

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """Classic environments for control. These environments are based on the MuJoCo environments provided by OpenAI. Reference: https://github.com/openai/gym/tree/master/gym/envs/mujoco """

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/ant/ant_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import omni.isaac.orbit.sim as sim_utils from omni.isaac.orbit.actuators import ImplicitActuatorCfg from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg from omni.isaac.orbit.envs import RLTaskEnvCfg from omni.isaac.orbit.managers import EventTermCfg as EventTerm from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm from omni.isaac.orbit.managers import RewardTermCfg as RewTerm from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.managers import TerminationTermCfg as DoneTerm from omni.isaac.orbit.scene import InteractiveSceneCfg from omni.isaac.orbit.terrains import TerrainImporterCfg from omni.isaac.orbit.utils import configclass from omni.isaac.orbit.utils.assets import ISAAC_NUCLEUS_DIR import omni.isaac.orbit_tasks.classic.humanoid.mdp as mdp ## # Scene definition ## @configclass class MySceneCfg(InteractiveSceneCfg): """Configuration for the terrain scene with an ant robot.""" # terrain terrain = TerrainImporterCfg( prim_path="/World/ground", terrain_type="plane", collision_group=-1, physics_material=sim_utils.RigidBodyMaterialCfg( friction_combine_mode="average", restitution_combine_mode="average", static_friction=1.0, dynamic_friction=1.0, restitution=0.0, ), debug_vis=False, ) # robot robot = ArticulationCfg( prim_path="{ENV_REGEX_NS}/Robot", spawn=sim_utils.UsdFileCfg( usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Ant/ant_instanceable.usd", rigid_props=sim_utils.RigidBodyPropertiesCfg( disable_gravity=False, max_depenetration_velocity=10.0, enable_gyroscopic_forces=True, ), articulation_props=sim_utils.ArticulationRootPropertiesCfg( enabled_self_collisions=False, solver_position_iteration_count=4, solver_velocity_iteration_count=0, sleep_threshold=0.005, stabilization_threshold=0.001, ), copy_from_source=False, ), init_state=ArticulationCfg.InitialStateCfg( pos=(0.0, 0.0, 0.5), joint_pos={".*": 0.0}, ), actuators={ "body": ImplicitActuatorCfg( joint_names_expr=[".*"], stiffness=0.0, damping=0.0, ), }, ) # lights light = AssetBaseCfg( prim_path="/World/light", spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), ) ## # MDP settings ## @configclass class CommandsCfg: """Command terms for the MDP.""" # no commands for this MDP null = mdp.NullCommandCfg() @configclass class ActionsCfg: """Action specifications for the MDP.""" joint_effort = mdp.JointEffortActionCfg(asset_name="robot", joint_names=[".*"], scale=7.5) @configclass class ObservationsCfg: """Observation specifications for the MDP.""" @configclass class PolicyCfg(ObsGroup): """Observations for the policy.""" base_height = ObsTerm(func=mdp.base_pos_z) base_lin_vel = ObsTerm(func=mdp.base_lin_vel) base_ang_vel = ObsTerm(func=mdp.base_ang_vel) base_yaw_roll = ObsTerm(func=mdp.base_yaw_roll) base_angle_to_target = ObsTerm(func=mdp.base_angle_to_target, params={"target_pos": (1000.0, 0.0, 0.0)}) base_up_proj = ObsTerm(func=mdp.base_up_proj) base_heading_proj = ObsTerm(func=mdp.base_heading_proj, params={"target_pos": (1000.0, 0.0, 0.0)}) joint_pos_norm = ObsTerm(func=mdp.joint_pos_norm) joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel, scale=0.2) feet_body_forces = ObsTerm( func=mdp.body_incoming_wrench, scale=0.1, params={ "asset_cfg": SceneEntityCfg( "robot", body_names=["front_left_foot", "front_right_foot", "left_back_foot", "right_back_foot"] ) }, ) actions = ObsTerm(func=mdp.last_action) def __post_init__(self): self.enable_corruption = False self.concatenate_terms = True # observation groups policy: PolicyCfg = PolicyCfg() @configclass class EventCfg: """Configuration for events.""" reset_base = EventTerm( func=mdp.reset_root_state_uniform, mode="reset", params={"pose_range": {}, "velocity_range": {}}, ) reset_robot_joints = EventTerm( func=mdp.reset_joints_by_offset, mode="reset", params={ "position_range": (-0.2, 0.2), "velocity_range": (-0.1, 0.1), }, ) @configclass class RewardsCfg: """Reward terms for the MDP.""" # (1) Reward for moving forward progress = RewTerm(func=mdp.progress_reward, weight=1.0, params={"target_pos": (1000.0, 0.0, 0.0)}) # (2) Stay alive bonus alive = RewTerm(func=mdp.is_alive, weight=0.5) # (3) Reward for non-upright posture upright = RewTerm(func=mdp.upright_posture_bonus, weight=0.1, params={"threshold": 0.93}) # (4) Reward for moving in the right direction move_to_target = RewTerm( func=mdp.move_to_target_bonus, weight=0.5, params={"threshold": 0.8, "target_pos": (1000.0, 0.0, 0.0)} ) # (5) Penalty for large action commands action_l2 = RewTerm(func=mdp.action_l2, weight=-0.005) # (6) Penalty for energy consumption energy = RewTerm(func=mdp.power_consumption, weight=-0.05, params={"gear_ratio": {".*": 15.0}}) # (7) Penalty for reaching close to joint limits joint_limits = RewTerm( func=mdp.joint_limits_penalty_ratio, weight=-0.1, params={"threshold": 0.99, "gear_ratio": {".*": 15.0}} ) @configclass class TerminationsCfg: """Termination terms for the MDP.""" # (1) Terminate if the episode length is exceeded time_out = DoneTerm(func=mdp.time_out, time_out=True) # (2) Terminate if the robot falls torso_height = DoneTerm(func=mdp.base_height, params={"minimum_height": 0.31}) @configclass class CurriculumCfg: """Curriculum terms for the MDP.""" pass @configclass class AntEnvCfg(RLTaskEnvCfg): """Configuration for the MuJoCo-style Ant walking environment.""" # Scene settings scene: MySceneCfg = MySceneCfg(num_envs=4096, env_spacing=5.0) # Basic settings observations: ObservationsCfg = ObservationsCfg() actions: ActionsCfg = ActionsCfg() commands: CommandsCfg = CommandsCfg() # MDP settings rewards: RewardsCfg = RewardsCfg() terminations: TerminationsCfg = TerminationsCfg() events: EventCfg = EventCfg() curriculum: CurriculumCfg = CurriculumCfg() def __post_init__(self): """Post initialization.""" # general settings self.decimation = 2 self.episode_length_s = 16.0 # simulation settings self.sim.dt = 1 / 120.0 self.sim.physx.bounce_threshold_velocity = 0.2 # default friction material self.sim.physics_material.static_friction = 1.0 self.sim.physics_material.dynamic_friction = 1.0 self.sim.physics_material.restitution = 0.0

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/ant/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """ Ant locomotion environment (similar to OpenAI Gym Ant-v2). """ import gymnasium as gym from . import agents, ant_env_cfg ## # Register Gym environments. ## gym.register( id="Isaac-Ant-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": ant_env_cfg.AntEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_ppo_cfg.AntPPORunnerCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", }, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/ant/agents/rsl_rl_ppo_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from omni.isaac.orbit.utils import configclass from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import ( RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg, ) @configclass class AntPPORunnerCfg(RslRlOnPolicyRunnerCfg): num_steps_per_env = 32 max_iterations = 1000 save_interval = 50 experiment_name = "ant" empirical_normalization = False policy = RslRlPpoActorCriticCfg( init_noise_std=1.0, actor_hidden_dims=[400, 200, 100], critic_hidden_dims=[400, 200, 100], activation="elu", ) algorithm = RslRlPpoAlgorithmCfg( value_loss_coef=1.0, use_clipped_value_loss=True, clip_param=0.2, entropy_coef=0.0, num_learning_epochs=5, num_mini_batches=4, learning_rate=5.0e-4, schedule="adaptive", gamma=0.99, lam=0.95, desired_kl=0.01, max_grad_norm=1.0, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/cartpole/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """ Cartpole balancing environment. """ import gymnasium as gym from . import agents from .cartpole_env_cfg import CartpoleEnvCfg ## # Register Gym environments. ## gym.register( id="Isaac-Cartpole-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": CartpoleEnvCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "rsl_rl_cfg_entry_point": agents.rsl_rl_ppo_cfg.CartpolePPORunnerCfg, "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", }, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/cartpole/cartpole_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import math import omni.isaac.orbit.sim as sim_utils from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg from omni.isaac.orbit.envs import RLTaskEnvCfg from omni.isaac.orbit.managers import EventTermCfg as EventTerm from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm from omni.isaac.orbit.managers import RewardTermCfg as RewTerm from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.managers import TerminationTermCfg as DoneTerm from omni.isaac.orbit.scene import InteractiveSceneCfg from omni.isaac.orbit.utils import configclass import omni.isaac.orbit_tasks.classic.cartpole.mdp as mdp ## # Pre-defined configs ## from omni.isaac.orbit_assets.cartpole import CARTPOLE_CFG # isort:skip ## # Scene definition ## @configclass class CartpoleSceneCfg(InteractiveSceneCfg): """Configuration for a cart-pole scene.""" # ground plane ground = AssetBaseCfg( prim_path="/World/ground", spawn=sim_utils.GroundPlaneCfg(size=(100.0, 100.0)), ) # cartpole robot: ArticulationCfg = CARTPOLE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") # lights dome_light = AssetBaseCfg( prim_path="/World/DomeLight", spawn=sim_utils.DomeLightCfg(color=(0.9, 0.9, 0.9), intensity=500.0), ) distant_light = AssetBaseCfg( prim_path="/World/DistantLight", spawn=sim_utils.DistantLightCfg(color=(0.9, 0.9, 0.9), intensity=2500.0), init_state=AssetBaseCfg.InitialStateCfg(rot=(0.738, 0.477, 0.477, 0.0)), ) ## # MDP settings ## @configclass class CommandsCfg: """Command terms for the MDP.""" # no commands for this MDP null = mdp.NullCommandCfg() @configclass class ActionsCfg: """Action specifications for the MDP.""" joint_effort = mdp.JointEffortActionCfg(asset_name="robot", joint_names=["slider_to_cart"], scale=100.0) @configclass class ObservationsCfg: """Observation specifications for the MDP.""" @configclass class PolicyCfg(ObsGroup): """Observations for policy group.""" # observation terms (order preserved) joint_pos_rel = ObsTerm(func=mdp.joint_pos_rel) joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel) def __post_init__(self) -> None: self.enable_corruption = False self.concatenate_terms = True # observation groups policy: PolicyCfg = PolicyCfg() @configclass class EventCfg: """Configuration for events.""" # reset reset_cart_position = EventTerm( func=mdp.reset_joints_by_offset, mode="reset", params={ "asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), "position_range": (-1.0, 1.0), "velocity_range": (-0.5, 0.5), }, ) reset_pole_position = EventTerm( func=mdp.reset_joints_by_offset, mode="reset", params={ "asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), "position_range": (-0.25 * math.pi, 0.25 * math.pi), "velocity_range": (-0.25 * math.pi, 0.25 * math.pi), }, ) @configclass class RewardsCfg: """Reward terms for the MDP.""" # (1) Constant running reward alive = RewTerm(func=mdp.is_alive, weight=1.0) # (2) Failure penalty terminating = RewTerm(func=mdp.is_terminated, weight=-2.0) # (3) Primary task: keep pole upright pole_pos = RewTerm( func=mdp.joint_pos_target_l2, weight=-1.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]), "target": 0.0}, ) # (4) Shaping tasks: lower cart velocity cart_vel = RewTerm( func=mdp.joint_vel_l1, weight=-0.01, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"])}, ) # (5) Shaping tasks: lower pole angular velocity pole_vel = RewTerm( func=mdp.joint_vel_l1, weight=-0.005, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"])}, ) @configclass class TerminationsCfg: """Termination terms for the MDP.""" # (1) Time out time_out = DoneTerm(func=mdp.time_out, time_out=True) # (2) Cart out of bounds cart_out_of_bounds = DoneTerm( func=mdp.joint_pos_manual_limit, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]), "bounds": (-3.0, 3.0)}, ) @configclass class CurriculumCfg: """Configuration for the curriculum.""" pass ## # Environment configuration ## @configclass class CartpoleEnvCfg(RLTaskEnvCfg): """Configuration for the locomotion velocity-tracking environment.""" # Scene settings scene: CartpoleSceneCfg = CartpoleSceneCfg(num_envs=4096, env_spacing=4.0) # Basic settings observations: ObservationsCfg = ObservationsCfg() actions: ActionsCfg = ActionsCfg() events: EventCfg = EventCfg() # MDP settings curriculum: CurriculumCfg = CurriculumCfg() rewards: RewardsCfg = RewardsCfg() terminations: TerminationsCfg = TerminationsCfg() # No command generator commands: CommandsCfg = CommandsCfg() # Post initialization def __post_init__(self) -> None: """Post initialization.""" # general settings self.decimation = 2 self.episode_length_s = 5 # viewer settings self.viewer.eye = (8.0, 0.0, 5.0) # simulation settings self.sim.dt = 1 / 120

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/cartpole/mdp/rewards.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import torch from typing import TYPE_CHECKING from omni.isaac.orbit.assets import Articulation from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.utils.math import wrap_to_pi if TYPE_CHECKING: from omni.isaac.orbit.envs import RLTaskEnv def joint_pos_target_l2(env: RLTaskEnv, target: float, asset_cfg: SceneEntityCfg) -> torch.Tensor: """Penalize joint position deviation from a target value.""" # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[asset_cfg.name] # wrap the joint positions to (-pi, pi) joint_pos = wrap_to_pi(asset.data.joint_pos[:, asset_cfg.joint_ids]) # compute the reward return torch.sum(torch.square(joint_pos - target), dim=1)

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/humanoid/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """ Humanoid locomotion environment (similar to OpenAI Gym Humanoid-v2). """ import gymnasium as gym from . import agents, humanoid_env_cfg ## # Register Gym environments. ## gym.register( id="Isaac-Humanoid-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": humanoid_env_cfg.HumanoidEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_ppo_cfg.HumanoidPPORunnerCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", "sb3_cfg_entry_point": f"{agents.__name__}:sb3_ppo_cfg.yaml", }, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/humanoid/humanoid_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import omni.isaac.orbit.sim as sim_utils from omni.isaac.orbit.actuators import ImplicitActuatorCfg from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg from omni.isaac.orbit.envs import RLTaskEnvCfg from omni.isaac.orbit.managers import EventTermCfg as EventTerm from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm from omni.isaac.orbit.managers import RewardTermCfg as RewTerm from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.managers import TerminationTermCfg as DoneTerm from omni.isaac.orbit.scene import InteractiveSceneCfg from omni.isaac.orbit.terrains import TerrainImporterCfg from omni.isaac.orbit.utils import configclass from omni.isaac.orbit.utils.assets import ISAAC_NUCLEUS_DIR import omni.isaac.orbit_tasks.classic.humanoid.mdp as mdp ## # Scene definition ## @configclass class MySceneCfg(InteractiveSceneCfg): """Configuration for the terrain scene with a humanoid robot.""" # terrain terrain = TerrainImporterCfg( prim_path="/World/ground", terrain_type="plane", collision_group=-1, physics_material=sim_utils.RigidBodyMaterialCfg(static_friction=1.0, dynamic_friction=1.0, restitution=0.0), debug_vis=False, ) # robot robot = ArticulationCfg( prim_path="{ENV_REGEX_NS}/Robot", spawn=sim_utils.UsdFileCfg( usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Humanoid/humanoid_instanceable.usd", rigid_props=sim_utils.RigidBodyPropertiesCfg( disable_gravity=None, max_depenetration_velocity=10.0, enable_gyroscopic_forces=True, ), articulation_props=sim_utils.ArticulationRootPropertiesCfg( enabled_self_collisions=True, solver_position_iteration_count=4, solver_velocity_iteration_count=0, sleep_threshold=0.005, stabilization_threshold=0.001, ), copy_from_source=False, ), init_state=ArticulationCfg.InitialStateCfg( pos=(0.0, 0.0, 1.34), joint_pos={".*": 0.0}, ), actuators={ "body": ImplicitActuatorCfg( joint_names_expr=[".*"], stiffness={ ".*_waist.*": 20.0, ".*_upper_arm.*": 10.0, "pelvis": 10.0, ".*_lower_arm": 2.0, ".*_thigh:0": 10.0, ".*_thigh:1": 20.0, ".*_thigh:2": 10.0, ".*_shin": 5.0, ".*_foot.*": 2.0, }, damping={ ".*_waist.*": 5.0, ".*_upper_arm.*": 5.0, "pelvis": 5.0, ".*_lower_arm": 1.0, ".*_thigh:0": 5.0, ".*_thigh:1": 5.0, ".*_thigh:2": 5.0, ".*_shin": 0.1, ".*_foot.*": 1.0, }, ), }, ) # lights light = AssetBaseCfg( prim_path="/World/light", spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), ) ## # MDP settings ## @configclass class CommandsCfg: """Command terms for the MDP.""" # no commands for this MDP null = mdp.NullCommandCfg() @configclass class ActionsCfg: """Action specifications for the MDP.""" joint_effort = mdp.JointEffortActionCfg( asset_name="robot", joint_names=[".*"], scale={ ".*_waist.*": 67.5, ".*_upper_arm.*": 67.5, "pelvis": 67.5, ".*_lower_arm": 45.0, ".*_thigh:0": 45.0, ".*_thigh:1": 135.0, ".*_thigh:2": 45.0, ".*_shin": 90.0, ".*_foot.*": 22.5, }, ) @configclass class ObservationsCfg: """Observation specifications for the MDP.""" @configclass class PolicyCfg(ObsGroup): """Observations for the policy.""" base_height = ObsTerm(func=mdp.base_pos_z) base_lin_vel = ObsTerm(func=mdp.base_lin_vel) base_ang_vel = ObsTerm(func=mdp.base_ang_vel, scale=0.25) base_yaw_roll = ObsTerm(func=mdp.base_yaw_roll) base_angle_to_target = ObsTerm(func=mdp.base_angle_to_target, params={"target_pos": (1000.0, 0.0, 0.0)}) base_up_proj = ObsTerm(func=mdp.base_up_proj) base_heading_proj = ObsTerm(func=mdp.base_heading_proj, params={"target_pos": (1000.0, 0.0, 0.0)}) joint_pos_norm = ObsTerm(func=mdp.joint_pos_norm) joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel, scale=0.1) feet_body_forces = ObsTerm( func=mdp.body_incoming_wrench, scale=0.01, params={"asset_cfg": SceneEntityCfg("robot", body_names=["left_foot", "right_foot"])}, ) actions = ObsTerm(func=mdp.last_action) def __post_init__(self): self.enable_corruption = False self.concatenate_terms = True # observation groups policy: PolicyCfg = PolicyCfg() @configclass class EventCfg: """Configuration for events.""" reset_base = EventTerm( func=mdp.reset_root_state_uniform, mode="reset", params={"pose_range": {}, "velocity_range": {}}, ) reset_robot_joints = EventTerm( func=mdp.reset_joints_by_offset, mode="reset", params={ "position_range": (-0.2, 0.2), "velocity_range": (-0.1, 0.1), }, ) @configclass class RewardsCfg: """Reward terms for the MDP.""" # (1) Reward for moving forward progress = RewTerm(func=mdp.progress_reward, weight=1.0, params={"target_pos": (1000.0, 0.0, 0.0)}) # (2) Stay alive bonus alive = RewTerm(func=mdp.is_alive, weight=2.0) # (3) Reward for non-upright posture upright = RewTerm(func=mdp.upright_posture_bonus, weight=0.1, params={"threshold": 0.93}) # (4) Reward for moving in the right direction move_to_target = RewTerm( func=mdp.move_to_target_bonus, weight=0.5, params={"threshold": 0.8, "target_pos": (1000.0, 0.0, 0.0)} ) # (5) Penalty for large action commands action_l2 = RewTerm(func=mdp.action_l2, weight=-0.01) # (6) Penalty for energy consumption energy = RewTerm( func=mdp.power_consumption, weight=-0.005, params={ "gear_ratio": { ".*_waist.*": 67.5, ".*_upper_arm.*": 67.5, "pelvis": 67.5, ".*_lower_arm": 45.0, ".*_thigh:0": 45.0, ".*_thigh:1": 135.0, ".*_thigh:2": 45.0, ".*_shin": 90.0, ".*_foot.*": 22.5, } }, ) # (7) Penalty for reaching close to joint limits joint_limits = RewTerm( func=mdp.joint_limits_penalty_ratio, weight=-0.25, params={ "threshold": 0.98, "gear_ratio": { ".*_waist.*": 67.5, ".*_upper_arm.*": 67.5, "pelvis": 67.5, ".*_lower_arm": 45.0, ".*_thigh:0": 45.0, ".*_thigh:1": 135.0, ".*_thigh:2": 45.0, ".*_shin": 90.0, ".*_foot.*": 22.5, }, }, ) @configclass class TerminationsCfg: """Termination terms for the MDP.""" # (1) Terminate if the episode length is exceeded time_out = DoneTerm(func=mdp.time_out, time_out=True) # (2) Terminate if the robot falls torso_height = DoneTerm(func=mdp.base_height, params={"minimum_height": 0.8}) @configclass class CurriculumCfg: """Curriculum terms for the MDP.""" pass @configclass class HumanoidEnvCfg(RLTaskEnvCfg): """Configuration for the MuJoCo-style Humanoid walking environment.""" # Scene settings scene: MySceneCfg = MySceneCfg(num_envs=4096, env_spacing=5.0) # Basic settings observations: ObservationsCfg = ObservationsCfg() actions: ActionsCfg = ActionsCfg() commands: CommandsCfg = CommandsCfg() # MDP settings rewards: RewardsCfg = RewardsCfg() terminations: TerminationsCfg = TerminationsCfg() events: EventCfg = EventCfg() curriculum: CurriculumCfg = CurriculumCfg() def __post_init__(self): """Post initialization.""" # general settings self.decimation = 2 self.episode_length_s = 16.0 # simulation settings self.sim.dt = 1 / 120.0 self.sim.physx.bounce_threshold_velocity = 0.2 # default friction material self.sim.physics_material.static_friction = 1.0 self.sim.physics_material.dynamic_friction = 1.0 self.sim.physics_material.restitution = 0.0

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/humanoid/mdp/rewards.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import torch from typing import TYPE_CHECKING import omni.isaac.orbit.utils.math as math_utils import omni.isaac.orbit.utils.string as string_utils from omni.isaac.orbit.assets import Articulation from omni.isaac.orbit.managers import ManagerTermBase, RewardTermCfg, SceneEntityCfg from . import observations as obs if TYPE_CHECKING: from omni.isaac.orbit.envs import RLTaskEnv def upright_posture_bonus( env: RLTaskEnv, threshold: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") ) -> torch.Tensor: """Reward for maintaining an upright posture.""" up_proj = obs.base_up_proj(env, asset_cfg).squeeze(-1) return (up_proj > threshold).float() def move_to_target_bonus( env: RLTaskEnv, threshold: float, target_pos: tuple[float, float, float], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), ) -> torch.Tensor: """Reward for moving to the target heading.""" heading_proj = obs.base_heading_proj(env, target_pos, asset_cfg).squeeze(-1) return torch.where(heading_proj > threshold, 1.0, heading_proj / threshold) class progress_reward(ManagerTermBase): """Reward for making progress towards the target.""" def __init__(self, env: RLTaskEnv, cfg: RewardTermCfg): # initialize the base class super().__init__(cfg, env) # create history buffer self.potentials = torch.zeros(env.num_envs, device=env.device) self.prev_potentials = torch.zeros_like(self.potentials) def reset(self, env_ids: torch.Tensor): # extract the used quantities (to enable type-hinting) asset: Articulation = self._env.scene["robot"] # compute projection of current heading to desired heading vector target_pos = torch.tensor(self.cfg.params["target_pos"], device=self.device) to_target_pos = target_pos - asset.data.root_pos_w[env_ids, :3] # reward terms self.potentials[env_ids] = -torch.norm(to_target_pos, p=2, dim=-1) / self._env.step_dt self.prev_potentials[env_ids] = self.potentials[env_ids] def __call__( self, env: RLTaskEnv, target_pos: tuple[float, float, float], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"), ) -> torch.Tensor: # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[asset_cfg.name] # compute vector to target target_pos = torch.tensor(target_pos, device=env.device) to_target_pos = target_pos - asset.data.root_pos_w[:, :3] to_target_pos[:, 2] = 0.0 # update history buffer and compute new potential self.prev_potentials[:] = self.potentials[:] self.potentials[:] = -torch.norm(to_target_pos, p=2, dim=-1) / env.step_dt return self.potentials - self.prev_potentials class joint_limits_penalty_ratio(ManagerTermBase): """Penalty for violating joint limits weighted by the gear ratio.""" def __init__(self, env: RLTaskEnv, cfg: RewardTermCfg): # add default argument if "asset_cfg" not in cfg.params: cfg.params["asset_cfg"] = SceneEntityCfg("robot") # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[cfg.params["asset_cfg"].name] # resolve the gear ratio for each joint self.gear_ratio = torch.ones(env.num_envs, asset.num_joints, device=env.device) index_list, _, value_list = string_utils.resolve_matching_names_values( cfg.params["gear_ratio"], asset.joint_names ) self.gear_ratio[:, index_list] = torch.tensor(value_list, device=env.device) self.gear_ratio_scaled = self.gear_ratio / torch.max(self.gear_ratio) def __call__( self, env: RLTaskEnv, threshold: float, gear_ratio: dict[str, float], asset_cfg: SceneEntityCfg ) -> torch.Tensor: # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[asset_cfg.name] # compute the penalty over normalized joints joint_pos_scaled = math_utils.scale_transform( asset.data.joint_pos, asset.data.soft_joint_pos_limits[..., 0], asset.data.soft_joint_pos_limits[..., 1] ) # scale the violation amount by the gear ratio violation_amount = (torch.abs(joint_pos_scaled) - threshold) / (1 - threshold) violation_amount = violation_amount * self.gear_ratio_scaled return torch.sum((torch.abs(joint_pos_scaled) > threshold) * violation_amount, dim=-1) class power_consumption(ManagerTermBase): """Penalty for the power consumed by the actions to the environment. This is computed as commanded torque times the joint velocity. """ def __init__(self, env: RLTaskEnv, cfg: RewardTermCfg): # add default argument if "asset_cfg" not in cfg.params: cfg.params["asset_cfg"] = SceneEntityCfg("robot") # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[cfg.params["asset_cfg"].name] # resolve the gear ratio for each joint self.gear_ratio = torch.ones(env.num_envs, asset.num_joints, device=env.device) index_list, _, value_list = string_utils.resolve_matching_names_values( cfg.params["gear_ratio"], asset.joint_names ) self.gear_ratio[:, index_list] = torch.tensor(value_list, device=env.device) self.gear_ratio_scaled = self.gear_ratio / torch.max(self.gear_ratio) def __call__(self, env: RLTaskEnv, gear_ratio: dict[str, float], asset_cfg: SceneEntityCfg) -> torch.Tensor: # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[asset_cfg.name] # return power = torque * velocity (here actions: joint torques) return torch.sum(torch.abs(env.action_manager.action * asset.data.joint_vel * self.gear_ratio_scaled), dim=-1)

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/classic/humanoid/mdp/observations.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import torch from typing import TYPE_CHECKING import omni.isaac.orbit.utils.math as math_utils from omni.isaac.orbit.assets import Articulation from omni.isaac.orbit.managers import SceneEntityCfg if TYPE_CHECKING: from omni.isaac.orbit.envs import BaseEnv def base_yaw_roll(env: BaseEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: """Yaw and roll of the base in the simulation world frame.""" # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[asset_cfg.name] # extract euler angles (in world frame) roll, _, yaw = math_utils.euler_xyz_from_quat(asset.data.root_quat_w) # normalize angle to [-pi, pi] roll = torch.atan2(torch.sin(roll), torch.cos(roll)) yaw = torch.atan2(torch.sin(yaw), torch.cos(yaw)) return torch.cat((yaw.unsqueeze(-1), roll.unsqueeze(-1)), dim=-1) def base_up_proj(env: BaseEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: """Projection of the base up vector onto the world up vector.""" # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[asset_cfg.name] # compute base up vector base_up_vec = math_utils.quat_rotate(asset.data.root_quat_w, -asset.GRAVITY_VEC_W) return base_up_vec[:, 2].unsqueeze(-1) def base_heading_proj( env: BaseEnv, target_pos: tuple[float, float, float], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") ) -> torch.Tensor: """Projection of the base forward vector onto the world forward vector.""" # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[asset_cfg.name] # compute desired heading direction to_target_pos = torch.tensor(target_pos, device=env.device) - asset.data.root_pos_w[:, :3] to_target_pos[:, 2] = 0.0 to_target_dir = math_utils.normalize(to_target_pos) # compute base forward vector heading_vec = math_utils.quat_rotate(asset.data.root_quat_w, asset.FORWARD_VEC_B) # compute dot product between heading and target direction heading_proj = torch.bmm(heading_vec.view(env.num_envs, 1, 3), to_target_dir.view(env.num_envs, 3, 1)) return heading_proj.view(env.num_envs, 1) def base_angle_to_target( env: BaseEnv, target_pos: tuple[float, float, float], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") ) -> torch.Tensor: """Angle between the base forward vector and the vector to the target.""" # extract the used quantities (to enable type-hinting) asset: Articulation = env.scene[asset_cfg.name] # compute desired heading direction to_target_pos = torch.tensor(target_pos, device=env.device) - asset.data.root_pos_w[:, :3] walk_target_angle = torch.atan2(to_target_pos[:, 1], to_target_pos[:, 0]) # compute base forward vector _, _, yaw = math_utils.euler_xyz_from_quat(asset.data.root_quat_w) # normalize angle to target to [-pi, pi] angle_to_target = walk_target_angle - yaw angle_to_target = torch.atan2(torch.sin(angle_to_target), torch.cos(angle_to_target)) return angle_to_target.unsqueeze(-1)

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """Manipulation environments for fixed-arm robots.""" from .reach import * # noqa

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/reach/reach_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations from dataclasses import MISSING import omni.isaac.orbit.sim as sim_utils from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg from omni.isaac.orbit.envs import RLTaskEnvCfg from omni.isaac.orbit.managers import ActionTermCfg as ActionTerm from omni.isaac.orbit.managers import CurriculumTermCfg as CurrTerm from omni.isaac.orbit.managers import EventTermCfg as EventTerm from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm from omni.isaac.orbit.managers import RewardTermCfg as RewTerm from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.managers import TerminationTermCfg as DoneTerm from omni.isaac.orbit.scene import InteractiveSceneCfg from omni.isaac.orbit.utils import configclass from omni.isaac.orbit.utils.assets import ISAAC_NUCLEUS_DIR from omni.isaac.orbit.utils.noise import AdditiveUniformNoiseCfg as Unoise import omni.isaac.orbit_tasks.manipulation.reach.mdp as mdp ## # Scene definition ## @configclass class ReachSceneCfg(InteractiveSceneCfg): """Configuration for the scene with a robotic arm.""" # world ground = AssetBaseCfg( prim_path="/World/ground", spawn=sim_utils.GroundPlaneCfg(), init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, -1.05)), ) table = AssetBaseCfg( prim_path="{ENV_REGEX_NS}/Table", spawn=sim_utils.UsdFileCfg( usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd", ), init_state=AssetBaseCfg.InitialStateCfg(pos=(0.55, 0.0, 0.0), rot=(0.70711, 0.0, 0.0, 0.70711)), ) # robots robot: ArticulationCfg = MISSING # lights light = AssetBaseCfg( prim_path="/World/light", spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=2500.0), ) ## # MDP settings ## @configclass class CommandsCfg: """Command terms for the MDP.""" ee_pose = mdp.UniformPoseCommandCfg( asset_name="robot", body_name=MISSING, resampling_time_range=(4.0, 4.0), debug_vis=True, ranges=mdp.UniformPoseCommandCfg.Ranges( pos_x=(0.35, 0.65), pos_y=(-0.2, 0.2), pos_z=(0.15, 0.5), roll=(0.0, 0.0), pitch=MISSING, # depends on end-effector axis yaw=(-3.14, 3.14), ), ) @configclass class ActionsCfg: """Action specifications for the MDP.""" arm_action: ActionTerm = MISSING gripper_action: ActionTerm | None = None @configclass class ObservationsCfg: """Observation specifications for the MDP.""" @configclass class PolicyCfg(ObsGroup): """Observations for policy group.""" # observation terms (order preserved) joint_pos = ObsTerm(func=mdp.joint_pos_rel, noise=Unoise(n_min=-0.01, n_max=0.01)) joint_vel = ObsTerm(func=mdp.joint_vel_rel, noise=Unoise(n_min=-0.01, n_max=0.01)) pose_command = ObsTerm(func=mdp.generated_commands, params={"command_name": "ee_pose"}) actions = ObsTerm(func=mdp.last_action) def __post_init__(self): self.enable_corruption = True self.concatenate_terms = True # observation groups policy: PolicyCfg = PolicyCfg() @configclass class EventCfg: """Configuration for events.""" reset_robot_joints = EventTerm( func=mdp.reset_joints_by_scale, mode="reset", params={ "position_range": (0.5, 1.5), "velocity_range": (0.0, 0.0), }, ) @configclass class RewardsCfg: """Reward terms for the MDP.""" # task terms end_effector_position_tracking = RewTerm( func=mdp.position_command_error, weight=-0.2, params={"asset_cfg": SceneEntityCfg("robot", body_names=MISSING), "command_name": "ee_pose"}, ) end_effector_orientation_tracking = RewTerm( func=mdp.orientation_command_error, weight=-0.05, params={"asset_cfg": SceneEntityCfg("robot", body_names=MISSING), "command_name": "ee_pose"}, ) # action penalty action_rate = RewTerm(func=mdp.action_rate_l2, weight=-0.0001) joint_vel = RewTerm( func=mdp.joint_vel_l2, weight=-0.0001, params={"asset_cfg": SceneEntityCfg("robot")}, ) @configclass class TerminationsCfg: """Termination terms for the MDP.""" time_out = DoneTerm(func=mdp.time_out, time_out=True) @configclass class CurriculumCfg: """Curriculum terms for the MDP.""" action_rate = CurrTerm( func=mdp.modify_reward_weight, params={"term_name": "action_rate", "weight": -0.005, "num_steps": 4500} ) ## # Environment configuration ## @configclass class ReachEnvCfg(RLTaskEnvCfg): """Configuration for the reach end-effector pose tracking environment.""" # Scene settings scene: ReachSceneCfg = ReachSceneCfg(num_envs=4096, env_spacing=2.5) # Basic settings observations: ObservationsCfg = ObservationsCfg() actions: ActionsCfg = ActionsCfg() commands: CommandsCfg = CommandsCfg() # MDP settings rewards: RewardsCfg = RewardsCfg() terminations: TerminationsCfg = TerminationsCfg() events: EventCfg = EventCfg() curriculum: CurriculumCfg = CurriculumCfg() def __post_init__(self): """Post initialization.""" # general settings self.decimation = 2 self.episode_length_s = 12.0 self.viewer.eye = (3.5, 3.5, 3.5) # simulation settings self.sim.dt = 1.0 / 60.0

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/reach/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """Fixed-arm environments with end-effector pose tracking commands."""

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/reach/mdp/rewards.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import torch from typing import TYPE_CHECKING from omni.isaac.orbit.assets import RigidObject from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.utils.math import combine_frame_transforms, quat_error_magnitude, quat_mul if TYPE_CHECKING: from omni.isaac.orbit.envs import RLTaskEnv def position_command_error(env: RLTaskEnv, command_name: str, asset_cfg: SceneEntityCfg) -> torch.Tensor: """Penalize tracking of the position error using L2-norm. The function computes the position error between the desired position (from the command) and the current position of the asset's body (in world frame). The position error is computed as the L2-norm of the difference between the desired and current positions. """ # extract the asset (to enable type hinting) asset: RigidObject = env.scene[asset_cfg.name] command = env.command_manager.get_command(command_name) # obtain the desired and current positions des_pos_b = command[:, :3] des_pos_w, _ = combine_frame_transforms(asset.data.root_state_w[:, :3], asset.data.root_state_w[:, 3:7], des_pos_b) curr_pos_w = asset.data.body_state_w[:, asset_cfg.body_ids[0], :3] # type: ignore return torch.norm(curr_pos_w - des_pos_w, dim=1) def orientation_command_error(env: RLTaskEnv, command_name: str, asset_cfg: SceneEntityCfg) -> torch.Tensor: """Penalize tracking orientation error using shortest path. The function computes the orientation error between the desired orientation (from the command) and the current orientation of the asset's body (in world frame). The orientation error is computed as the shortest path between the desired and current orientations. """ # extract the asset (to enable type hinting) asset: RigidObject = env.scene[asset_cfg.name] command = env.command_manager.get_command(command_name) # obtain the desired and current orientations des_quat_b = command[:, 3:7] des_quat_w = quat_mul(asset.data.root_state_w[:, 3:7], des_quat_b) curr_quat_w = asset.data.body_state_w[:, asset_cfg.body_ids[0], 3:7] # type: ignore return quat_error_magnitude(curr_quat_w, des_quat_w)

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/reach/config/franka/ik_rel_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from omni.isaac.orbit.controllers.differential_ik_cfg import DifferentialIKControllerCfg from omni.isaac.orbit.envs.mdp.actions.actions_cfg import DifferentialInverseKinematicsActionCfg from omni.isaac.orbit.utils import configclass from . import joint_pos_env_cfg ## # Pre-defined configs ## from omni.isaac.orbit_assets.franka import FRANKA_PANDA_HIGH_PD_CFG # isort: skip @configclass class FrankaReachEnvCfg(joint_pos_env_cfg.FrankaReachEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # Set Franka as robot # We switch here to a stiffer PD controller for IK tracking to be better. self.scene.robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") # Set actions for the specific robot type (franka) self.actions.body_joint_pos = DifferentialInverseKinematicsActionCfg( asset_name="robot", joint_names=["panda_joint.*"], body_name="panda_hand", controller=DifferentialIKControllerCfg(command_type="pose", use_relative_mode=True, ik_method="dls"), scale=0.5, body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(pos=[0.0, 0.0, 0.107]), ) @configclass class FrankaReachEnvCfg_PLAY(FrankaReachEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # make a smaller scene for play self.scene.num_envs = 50 self.scene.env_spacing = 2.5 # disable randomization for play self.observations.policy.enable_corruption = False

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/reach/config/franka/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import gymnasium as gym from . import agents, ik_abs_env_cfg, ik_rel_env_cfg, joint_pos_env_cfg ## # Register Gym environments. ## ## # Joint Position Control ## gym.register( id="Isaac-Reach-Franka-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": joint_pos_env_cfg.FrankaReachEnvCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_cfg:FrankaReachPPORunnerCfg", "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, ) gym.register( id="Isaac-Reach-Franka-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": joint_pos_env_cfg.FrankaReachEnvCfg_PLAY, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_cfg:FrankaReachPPORunnerCfg", "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, ) ## # Inverse Kinematics - Absolute Pose Control ## gym.register( id="Isaac-Reach-Franka-IK-Abs-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_abs_env_cfg.FrankaReachEnvCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_cfg:FrankaReachPPORunnerCfg", "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, disable_env_checker=True, ) gym.register( id="Isaac-Reach-Franka-IK-Abs-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_abs_env_cfg.FrankaReachEnvCfg_PLAY, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_cfg:FrankaReachPPORunnerCfg", "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, disable_env_checker=True, ) ## # Inverse Kinematics - Relative Pose Control ## gym.register( id="Isaac-Reach-Franka-IK-Rel-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_rel_env_cfg.FrankaReachEnvCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_cfg:FrankaReachPPORunnerCfg", "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, disable_env_checker=True, ) gym.register( id="Isaac-Reach-Franka-IK-Rel-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_rel_env_cfg.FrankaReachEnvCfg_PLAY, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_cfg:FrankaReachPPORunnerCfg", "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, disable_env_checker=True, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/reach/config/franka/joint_pos_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import math from omni.isaac.orbit.utils import configclass import omni.isaac.orbit_tasks.manipulation.reach.mdp as mdp from omni.isaac.orbit_tasks.manipulation.reach.reach_env_cfg import ReachEnvCfg ## # Pre-defined configs ## from omni.isaac.orbit_assets import FRANKA_PANDA_CFG # isort: skip ## # Environment configuration ## @configclass class FrankaReachEnvCfg(ReachEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # switch robot to franka self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") # override rewards self.rewards.end_effector_position_tracking.params["asset_cfg"].body_names = ["panda_hand"] self.rewards.end_effector_orientation_tracking.params["asset_cfg"].body_names = ["panda_hand"] # override actions self.actions.arm_action = mdp.JointPositionActionCfg( asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True ) # override command generator body # end-effector is along z-direction self.commands.ee_pose.body_name = "panda_hand" self.commands.ee_pose.ranges.pitch = (math.pi, math.pi) @configclass class FrankaReachEnvCfg_PLAY(FrankaReachEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # make a smaller scene for play self.scene.num_envs = 50 self.scene.env_spacing = 2.5 # disable randomization for play self.observations.policy.enable_corruption = False

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/reach/config/ur_10/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import gymnasium as gym from . import agents, joint_pos_env_cfg ## # Register Gym environments. ## gym.register( id="Isaac-Reach-UR10-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": joint_pos_env_cfg.UR10ReachEnvCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UR10ReachPPORunnerCfg", }, ) gym.register( id="Isaac-Reach-UR10-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": joint_pos_env_cfg.UR10ReachEnvCfg_PLAY, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UR10ReachPPORunnerCfg", }, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/reach/config/ur_10/joint_pos_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import math from omni.isaac.orbit.utils import configclass import omni.isaac.orbit_tasks.manipulation.reach.mdp as mdp from omni.isaac.orbit_tasks.manipulation.reach.reach_env_cfg import ReachEnvCfg ## # Pre-defined configs ## from omni.isaac.orbit_assets import UR10_CFG # isort: skip ## # Environment configuration ## @configclass class UR10ReachEnvCfg(ReachEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # switch robot to ur10 self.scene.robot = UR10_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") # override events self.events.reset_robot_joints.params["position_range"] = (0.75, 1.25) # override rewards self.rewards.end_effector_position_tracking.params["asset_cfg"].body_names = ["ee_link"] self.rewards.end_effector_orientation_tracking.params["asset_cfg"].body_names = ["ee_link"] # override actions self.actions.arm_action = mdp.JointPositionActionCfg( asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True ) # override command generator body # end-effector is along x-direction self.commands.ee_pose.body_name = "ee_link" self.commands.ee_pose.ranges.pitch = (math.pi / 2, math.pi / 2) @configclass class UR10ReachEnvCfg_PLAY(UR10ReachEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # make a smaller scene for play self.scene.num_envs = 50 self.scene.env_spacing = 2.5 # disable randomization for play self.observations.policy.enable_corruption = False

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/cabinet/cabinet_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause # Copyright (c) 2022-2023, The ORBIT Project Developers. # All rights reserved. # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations from dataclasses import MISSING import omni.isaac.orbit.sim as sim_utils from omni.isaac.orbit.actuators.actuator_cfg import ImplicitActuatorCfg from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg from omni.isaac.orbit.envs import RLTaskEnvCfg from omni.isaac.orbit.managers import EventTermCfg as EventTerm from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm from omni.isaac.orbit.managers import RewardTermCfg as RewTerm from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.managers import TerminationTermCfg as DoneTerm from omni.isaac.orbit.scene import InteractiveSceneCfg from omni.isaac.orbit.sensors import FrameTransformerCfg from omni.isaac.orbit.sensors.frame_transformer import OffsetCfg from omni.isaac.orbit.utils import configclass from omni.isaac.orbit.utils.assets import ISAAC_NUCLEUS_DIR from . import mdp ## # Pre-defined configs ## from omni.isaac.orbit.markers.config import FRAME_MARKER_CFG # isort: skip FRAME_MARKER_SMALL_CFG = FRAME_MARKER_CFG.copy() FRAME_MARKER_SMALL_CFG.markers["frame"].scale = (0.10, 0.10, 0.10) ## # Scene definition ## @configclass class CabinetSceneCfg(InteractiveSceneCfg): """Configuration for the cabinet scene with a robot and a cabinet. This is the abstract base implementation, the exact scene is defined in the derived classes which need to set the robot and end-effector frames """ # robots, Will be populated by agent env cfg robot: ArticulationCfg = MISSING # End-effector, Will be populated by agent env cfg ee_frame: FrameTransformerCfg = MISSING cabinet = ArticulationCfg( prim_path="{ENV_REGEX_NS}/Cabinet", spawn=sim_utils.UsdFileCfg( usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Sektion_Cabinet/sektion_cabinet_instanceable.usd", activate_contact_sensors=False, ), init_state=ArticulationCfg.InitialStateCfg( pos=(0.8, 0, 0.4), rot=(0.0, 0.0, 0.0, 1.0), joint_pos={ "door_left_joint": 0.0, "door_right_joint": 0.0, "drawer_bottom_joint": 0.0, "drawer_top_joint": 0.0, }, ), actuators={ "drawers": ImplicitActuatorCfg( joint_names_expr=["drawer_top_joint", "drawer_bottom_joint"], effort_limit=87.0, velocity_limit=100.0, stiffness=10.0, damping=1.0, ), "doors": ImplicitActuatorCfg( joint_names_expr=["door_left_joint", "door_right_joint"], effort_limit=87.0, velocity_limit=100.0, stiffness=10.0, damping=2.5, ), }, ) # Frame definitions for the cabinet. cabinet_frame = FrameTransformerCfg( prim_path="{ENV_REGEX_NS}/Cabinet/sektion", debug_vis=True, visualizer_cfg=FRAME_MARKER_SMALL_CFG.replace(prim_path="/Visuals/CabinetFrameTransformer"), target_frames=[ FrameTransformerCfg.FrameCfg( prim_path="{ENV_REGEX_NS}/Cabinet/drawer_handle_top", name="drawer_handle_top", offset=OffsetCfg( pos=(0.305, 0.0, 0.01), rot=(0.5, 0.5, -0.5, -0.5), # align with end-effector frame ), ), ], ) # plane plane = AssetBaseCfg( prim_path="/World/GroundPlane", init_state=AssetBaseCfg.InitialStateCfg(), spawn=sim_utils.GroundPlaneCfg(), collision_group=-1, ) # lights light = AssetBaseCfg( prim_path="/World/light", spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), ) ## # MDP settings ## @configclass class CommandsCfg: """Command terms for the MDP.""" null_command = mdp.NullCommandCfg() @configclass class ActionsCfg: """Action specifications for the MDP.""" body_joint_pos: mdp.JointPositionActionCfg = MISSING finger_joint_pos: mdp.BinaryJointPositionActionCfg = MISSING @configclass class ObservationsCfg: """Observation specifications for the MDP.""" @configclass class PolicyCfg(ObsGroup): """Observations for policy group.""" joint_pos = ObsTerm(func=mdp.joint_pos_rel) joint_vel = ObsTerm(func=mdp.joint_vel_rel) cabinet_joint_pos = ObsTerm( func=mdp.joint_pos_rel, params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_top_joint"])}, ) cabinet_joint_vel = ObsTerm( func=mdp.joint_vel_rel, params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_top_joint"])}, ) rel_ee_drawer_distance = ObsTerm(func=mdp.rel_ee_drawer_distance) actions = ObsTerm(func=mdp.last_action) def __post_init__(self): self.enable_corruption = True self.concatenate_terms = True # observation groups policy: PolicyCfg = PolicyCfg() @configclass class EventCfg: """Configuration for events.""" robot_physics_material = EventTerm( func=mdp.randomize_rigid_body_material, mode="startup", params={ "asset_cfg": SceneEntityCfg("robot", body_names=".*"), "static_friction_range": (0.8, 1.25), "dynamic_friction_range": (0.8, 1.25), "restitution_range": (0.0, 0.0), "num_buckets": 16, }, ) cabinet_physics_material = EventTerm( func=mdp.randomize_rigid_body_material, mode="startup", params={ "asset_cfg": SceneEntityCfg("cabinet", body_names="drawer_handle_top"), "static_friction_range": (1.0, 1.25), "dynamic_friction_range": (1.25, 1.5), "restitution_range": (0.0, 0.0), "num_buckets": 16, }, ) reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") reset_robot_joints = EventTerm( func=mdp.reset_joints_by_offset, mode="reset", params={ "position_range": (-0.1, 0.1), "velocity_range": (0.0, 0.0), }, ) @configclass class RewardsCfg: """Reward terms for the MDP.""" # 1. Approach the handle approach_ee_handle = RewTerm(func=mdp.approach_ee_handle, weight=2.0, params={"threshold": 0.2}) align_ee_handle = RewTerm(func=mdp.align_ee_handle, weight=0.5) # 2. Grasp the handle approach_gripper_handle = RewTerm(func=mdp.approach_gripper_handle, weight=5.0, params={"offset": MISSING}) align_grasp_around_handle = RewTerm(func=mdp.align_grasp_around_handle, weight=0.125) grasp_handle = RewTerm( func=mdp.grasp_handle, weight=0.5, params={ "threshold": 0.03, "open_joint_pos": MISSING, "asset_cfg": SceneEntityCfg("robot", joint_names=MISSING), }, ) # 3. Open the drawer open_drawer_bonus = RewTerm( func=mdp.open_drawer_bonus, weight=7.5, params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_top_joint"])}, ) multi_stage_open_drawer = RewTerm( func=mdp.multi_stage_open_drawer, weight=1.0, params={"asset_cfg": SceneEntityCfg("cabinet", joint_names=["drawer_top_joint"])}, ) # 4. Penalize actions for cosmetic reasons action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-1e-2) joint_vel = RewTerm(func=mdp.joint_vel_l2, weight=-0.0001) @configclass class TerminationsCfg: """Termination terms for the MDP.""" time_out = DoneTerm(func=mdp.time_out, time_out=True) ## # Environment configuration ## @configclass class CabinetEnvCfg(RLTaskEnvCfg): """Configuration for the cabinet environment.""" # Scene settings scene: CabinetSceneCfg = CabinetSceneCfg(num_envs=4096, env_spacing=2.0) # Basic settings observations: ObservationsCfg = ObservationsCfg() actions: ActionsCfg = ActionsCfg() commands: CommandsCfg = CommandsCfg() # MDP settings rewards: RewardsCfg = RewardsCfg() terminations: TerminationsCfg = TerminationsCfg() events: EventCfg = EventCfg() def __post_init__(self): """Post initialization.""" # general settings self.decimation = 1 self.episode_length_s = 8.0 self.viewer.eye = (-2.0, 2.0, 2.0) self.viewer.lookat = (0.8, 0.0, 0.5) # simulation settings self.sim.dt = 1 / 60 # 60Hz self.sim.physx.bounce_threshold_velocity = 0.2 self.sim.physx.bounce_threshold_velocity = 0.01 self.sim.physx.friction_correlation_distance = 0.00625

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/cabinet/mdp/rewards.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import torch from typing import TYPE_CHECKING from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.utils.math import matrix_from_quat if TYPE_CHECKING: from omni.isaac.orbit.envs import RLTaskEnv def approach_ee_handle(env: RLTaskEnv, threshold: float) -> torch.Tensor: r"""Reward the robot for reaching the drawer handle using inverse-square law. It uses a piecewise function to reward the robot for reaching the handle. .. math:: reward = \begin{cases} 2 * (1 / (1 + distance^2))^2 & \text{if } distance \leq threshold \\ (1 / (1 + distance^2))^2 & \text{otherwise} \end{cases} """ ee_tcp_pos = env.scene["ee_frame"].data.target_pos_w[..., 0, :] handle_pos = env.scene["cabinet_frame"].data.target_pos_w[..., 0, :] # Compute the distance of the end-effector to the handle distance = torch.norm(handle_pos - ee_tcp_pos, dim=-1, p=2) # Reward the robot for reaching the handle reward = 1.0 / (1.0 + distance**2) reward = torch.pow(reward, 2) return torch.where(distance <= threshold, 2 * reward, reward) def align_ee_handle(env: RLTaskEnv) -> torch.Tensor: """Reward for aligning the end-effector with the handle. The reward is based on the alignment of the gripper with the handle. It is computed as follows: .. math:: reward = 0.5 * (align_z^2 + align_x^2) where :math:`align_z` is the dot product of the z direction of the gripper and the -x direction of the handle and :math:`align_x` is the dot product of the x direction of the gripper and the -y direction of the handle. """ ee_tcp_quat = env.scene["ee_frame"].data.target_quat_w[..., 0, :] handle_quat = env.scene["cabinet_frame"].data.target_quat_w[..., 0, :] ee_tcp_rot_mat = matrix_from_quat(ee_tcp_quat) handle_mat = matrix_from_quat(handle_quat) # get current x and y direction of the handle handle_x, handle_y = handle_mat[..., 0], handle_mat[..., 1] # get current x and z direction of the gripper ee_tcp_x, ee_tcp_z = ee_tcp_rot_mat[..., 0], ee_tcp_rot_mat[..., 2] # make sure gripper aligns with the handle # in this case, the z direction of the gripper should be close to the -x direction of the handle # and the x direction of the gripper should be close to the -y direction of the handle # dot product of z and x should be large align_z = torch.bmm(ee_tcp_z.unsqueeze(1), -handle_x.unsqueeze(-1)).squeeze(-1).squeeze(-1) align_x = torch.bmm(ee_tcp_x.unsqueeze(1), -handle_y.unsqueeze(-1)).squeeze(-1).squeeze(-1) return 0.5 * (torch.sign(align_z) * align_z**2 + torch.sign(align_x) * align_x**2) def align_grasp_around_handle(env: RLTaskEnv) -> torch.Tensor: """Bonus for correct hand orientation around the handle. The correct hand orientation is when the left finger is above the handle and the right finger is below the handle. """ # Target object position: (num_envs, 3) handle_pos = env.scene["cabinet_frame"].data.target_pos_w[..., 0, :] # Fingertips position: (num_envs, n_fingertips, 3) ee_fingertips_w = env.scene["ee_frame"].data.target_pos_w[..., 1:, :] lfinger_pos = ee_fingertips_w[..., 0, :] rfinger_pos = ee_fingertips_w[..., 1, :] # Check if hand is in a graspable pose is_graspable = (rfinger_pos[:, 2] < handle_pos[:, 2]) & (lfinger_pos[:, 2] > handle_pos[:, 2]) # bonus if left finger is above the drawer handle and right below return is_graspable def approach_gripper_handle(env: RLTaskEnv, offset: float = 0.04) -> torch.Tensor: """Reward the robot's gripper reaching the drawer handle with the right pose. This function returns the distance of fingertips to the handle when the fingers are in a grasping orientation (i.e., the left finger is above the handle and the right finger is below the handle). Otherwise, it returns zero. """ # Target object position: (num_envs, 3) handle_pos = env.scene["cabinet_frame"].data.target_pos_w[..., 0, :] # Fingertips position: (num_envs, n_fingertips, 3) ee_fingertips_w = env.scene["ee_frame"].data.target_pos_w[..., 1:, :] lfinger_pos = ee_fingertips_w[..., 0, :] rfinger_pos = ee_fingertips_w[..., 1, :] # Compute the distance of each finger from the handle lfinger_dist = torch.abs(lfinger_pos[:, 2] - handle_pos[:, 2]) rfinger_dist = torch.abs(rfinger_pos[:, 2] - handle_pos[:, 2]) # Check if hand is in a graspable pose is_graspable = (rfinger_pos[:, 2] < handle_pos[:, 2]) & (lfinger_pos[:, 2] > handle_pos[:, 2]) return is_graspable * ((offset - lfinger_dist) + (offset - rfinger_dist)) def grasp_handle(env: RLTaskEnv, threshold: float, open_joint_pos: float, asset_cfg: SceneEntityCfg) -> torch.Tensor: """Reward for closing the fingers when being close to the handle. The :attr:`threshold` is the distance from the handle at which the fingers should be closed. The :attr:`open_joint_pos` is the joint position when the fingers are open. Note: It is assumed that zero joint position corresponds to the fingers being closed. """ ee_tcp_pos = env.scene["ee_frame"].data.target_pos_w[..., 0, :] handle_pos = env.scene["cabinet_frame"].data.target_pos_w[..., 0, :] gripper_joint_pos = env.scene[asset_cfg.name].data.joint_pos[:, asset_cfg.joint_ids] distance = torch.norm(handle_pos - ee_tcp_pos, dim=-1, p=2) is_close = distance <= threshold return is_close * torch.sum(open_joint_pos - gripper_joint_pos, dim=-1) def open_drawer_bonus(env: RLTaskEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: """Bonus for opening the drawer given by the joint position of the drawer. The bonus is given when the drawer is open. If the grasp is around the handle, the bonus is doubled. """ drawer_pos = env.scene[asset_cfg.name].data.joint_pos[:, asset_cfg.joint_ids[0]] is_graspable = align_grasp_around_handle(env).float() return (is_graspable + 1.0) * drawer_pos def multi_stage_open_drawer(env: RLTaskEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: """Multi-stage bonus for opening the drawer. Depending on the drawer's position, the reward is given in three stages: easy, medium, and hard. This helps the agent to learn to open the drawer in a controlled manner. """ drawer_pos = env.scene[asset_cfg.name].data.joint_pos[:, asset_cfg.joint_ids[0]] is_graspable = align_grasp_around_handle(env).float() open_easy = (drawer_pos > 0.01) * 0.5 open_medium = (drawer_pos > 0.2) * is_graspable open_hard = (drawer_pos > 0.3) * is_graspable return open_easy + open_medium + open_hard

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/cabinet/mdp/observations.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import torch from typing import TYPE_CHECKING from omni.isaac.orbit.assets import ArticulationData from omni.isaac.orbit.sensors import FrameTransformerData if TYPE_CHECKING: from omni.isaac.orbit.envs import RLTaskEnv def rel_ee_object_distance(env: RLTaskEnv) -> torch.Tensor: """The distance between the end-effector and the object.""" ee_tf_data: FrameTransformerData = env.scene["ee_frame"].data object_data: ArticulationData = env.scene["object"].data return object_data.root_pos_w - ee_tf_data.target_pos_w[..., 0, :] def rel_ee_drawer_distance(env: RLTaskEnv) -> torch.Tensor: """The distance between the end-effector and the object.""" ee_tf_data: FrameTransformerData = env.scene["ee_frame"].data cabinet_tf_data: FrameTransformerData = env.scene["cabinet_frame"].data return cabinet_tf_data.target_pos_w[..., 0, :] - ee_tf_data.target_pos_w[..., 0, :] def fingertips_pos(env: RLTaskEnv) -> torch.Tensor: """The position of the fingertips relative to the environment origins.""" ee_tf_data: FrameTransformerData = env.scene["ee_frame"].data fingertips_pos = ee_tf_data.target_pos_w[..., 1:, :] - env.scene.env_origins.unsqueeze(1) return fingertips_pos.view(env.num_envs, -1) def ee_pos(env: RLTaskEnv) -> torch.Tensor: """The position of the end-effector relative to the environment origins.""" ee_tf_data: FrameTransformerData = env.scene["ee_frame"].data ee_pos = ee_tf_data.target_pos_w[..., 0, :] - env.scene.env_origins return ee_pos def ee_quat(env: RLTaskEnv) -> torch.Tensor: """The orientation of the end-effector in the environment frame.""" ee_tf_data: FrameTransformerData = env.scene["ee_frame"].data ee_quat = ee_tf_data.target_quat_w[..., 0, :] # make first element of quaternion positive ee_quat[ee_quat[:, 0] < 0] *= -1 return ee_quat

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/cabinet/config/franka/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import gymnasium as gym from . import agents, ik_abs_env_cfg, ik_rel_env_cfg, joint_pos_env_cfg ## # Register Gym environments. ## ## # Joint Position Control ## gym.register( id="Isaac-Open-Drawer-Franka-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": joint_pos_env_cfg.FrankaCabinetEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CabinetPPORunnerCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", }, disable_env_checker=True, ) gym.register( id="Isaac-Open-Drawer-Franka-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": joint_pos_env_cfg.FrankaCabinetEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CabinetPPORunnerCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", }, disable_env_checker=True, ) ## # Inverse Kinematics - Absolute Pose Control ## gym.register( id="Isaac-Open-Drawer-Franka-IK-Abs-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_abs_env_cfg.FrankaCabinetEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CabinetPPORunnerCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", }, disable_env_checker=True, ) gym.register( id="Isaac-Open-Drawer-Franka-IK-Abs-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_abs_env_cfg.FrankaCabinetEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CabinetPPORunnerCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", }, disable_env_checker=True, ) ## # Inverse Kinematics - Relative Pose Control ## gym.register( id="Isaac-Open-Drawer-Franka-IK-Rel-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_rel_env_cfg.FrankaCabinetEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CabinetPPORunnerCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", }, disable_env_checker=True, ) gym.register( id="Isaac-Open-Drawer-Franka-IK-Rel-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_rel_env_cfg.FrankaCabinetEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CabinetPPORunnerCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml", }, disable_env_checker=True, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/cabinet/config/franka/joint_pos_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from omni.isaac.orbit.sensors import FrameTransformerCfg from omni.isaac.orbit.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg from omni.isaac.orbit.utils import configclass from omni.isaac.orbit_tasks.manipulation.cabinet import mdp from omni.isaac.orbit_tasks.manipulation.cabinet.cabinet_env_cfg import CabinetEnvCfg ## # Pre-defined configs ## from omni.isaac.orbit_assets.franka import FRANKA_PANDA_CFG # isort: skip from omni.isaac.orbit_tasks.manipulation.cabinet.cabinet_env_cfg import FRAME_MARKER_SMALL_CFG # isort: skip @configclass class FrankaCabinetEnvCfg(CabinetEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # Set franka as robot self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") # Set Actions for the specific robot type (franka) self.actions.body_joint_pos = mdp.JointPositionActionCfg( asset_name="robot", joint_names=["panda_joint.*"], scale=1.0, use_default_offset=True, ) self.actions.finger_joint_pos = mdp.BinaryJointPositionActionCfg( asset_name="robot", joint_names=["panda_finger.*"], open_command_expr={"panda_finger_.*": 0.04}, close_command_expr={"panda_finger_.*": 0.0}, ) # Listens to the required transforms # IMPORTANT: The order of the frames in the list is important. The first frame is the tool center point (TCP) # the other frames are the fingers self.scene.ee_frame = FrameTransformerCfg( prim_path="{ENV_REGEX_NS}/Robot/panda_link0", debug_vis=False, visualizer_cfg=FRAME_MARKER_SMALL_CFG.replace(prim_path="/Visuals/EndEffectorFrameTransformer"), target_frames=[ FrameTransformerCfg.FrameCfg( prim_path="{ENV_REGEX_NS}/Robot/panda_hand", name="ee_tcp", offset=OffsetCfg( pos=(0.0, 0.0, 0.1034), ), ), FrameTransformerCfg.FrameCfg( prim_path="{ENV_REGEX_NS}/Robot/panda_leftfinger", name="tool_leftfinger", offset=OffsetCfg( pos=(0.0, 0.0, 0.046), ), ), FrameTransformerCfg.FrameCfg( prim_path="{ENV_REGEX_NS}/Robot/panda_rightfinger", name="tool_rightfinger", offset=OffsetCfg( pos=(0.0, 0.0, 0.046), ), ), ], ) # override rewards self.rewards.approach_gripper_handle.params["offset"] = 0.04 self.rewards.grasp_handle.params["open_joint_pos"] = 0.04 self.rewards.grasp_handle.params["asset_cfg"].joint_names = ["panda_finger_.*"] @configclass class FrankaCabinetEnvCfg_PLAY(FrankaCabinetEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # make a smaller scene for play self.scene.num_envs = 50 self.scene.env_spacing = 2.5 # disable randomization for play self.observations.policy.enable_corruption = False

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/lift/lift_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations from dataclasses import MISSING import omni.isaac.orbit.sim as sim_utils from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg from omni.isaac.orbit.envs import RLTaskEnvCfg from omni.isaac.orbit.managers import CurriculumTermCfg as CurrTerm from omni.isaac.orbit.managers import EventTermCfg as EventTerm from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm from omni.isaac.orbit.managers import RewardTermCfg as RewTerm from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.managers import TerminationTermCfg as DoneTerm from omni.isaac.orbit.scene import InteractiveSceneCfg from omni.isaac.orbit.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg from omni.isaac.orbit.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg, UsdFileCfg from omni.isaac.orbit.utils import configclass from omni.isaac.orbit.utils.assets import ISAAC_NUCLEUS_DIR from . import mdp ## # Scene definition ## @configclass class ObjectTableSceneCfg(InteractiveSceneCfg): """Configuration for the lift scene with a robot and a object. This is the abstract base implementation, the exact scene is defined in the derived classes which need to set the target object, robot and end-effector frames """ # robots: will be populated by agent env cfg robot: ArticulationCfg = MISSING # end-effector sensor: will be populated by agent env cfg ee_frame: FrameTransformerCfg = MISSING # target object: will be populated by agent env cfg object: RigidObjectCfg = MISSING # Table table = AssetBaseCfg( prim_path="{ENV_REGEX_NS}/Table", init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]), spawn=UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"), ) # plane plane = AssetBaseCfg( prim_path="/World/GroundPlane", init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, -1.05]), spawn=GroundPlaneCfg(), ) # lights light = AssetBaseCfg( prim_path="/World/light", spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), ) ## # MDP settings ## @configclass class CommandsCfg: """Command terms for the MDP.""" object_pose = mdp.UniformPoseCommandCfg( asset_name="robot", body_name=MISSING, # will be set by agent env cfg resampling_time_range=(5.0, 5.0), debug_vis=True, ranges=mdp.UniformPoseCommandCfg.Ranges( pos_x=(0.4, 0.6), pos_y=(-0.25, 0.25), pos_z=(0.25, 0.5), roll=(0.0, 0.0), pitch=(0.0, 0.0), yaw=(0.0, 0.0) ), ) @configclass class ActionsCfg: """Action specifications for the MDP.""" # will be set by agent env cfg body_joint_pos: mdp.JointPositionActionCfg = MISSING finger_joint_pos: mdp.BinaryJointPositionActionCfg = MISSING @configclass class ObservationsCfg: """Observation specifications for the MDP.""" @configclass class PolicyCfg(ObsGroup): """Observations for policy group.""" joint_pos = ObsTerm(func=mdp.joint_pos_rel) joint_vel = ObsTerm(func=mdp.joint_vel_rel) object_position = ObsTerm(func=mdp.object_position_in_robot_root_frame) target_object_position = ObsTerm(func=mdp.generated_commands, params={"command_name": "object_pose"}) actions = ObsTerm(func=mdp.last_action) def __post_init__(self): self.enable_corruption = True self.concatenate_terms = True # observation groups policy: PolicyCfg = PolicyCfg() @configclass class EventCfg: """Configuration for events.""" reset_all = EventTerm(func=mdp.reset_scene_to_default, mode="reset") reset_object_position = EventTerm( func=mdp.reset_root_state_uniform, mode="reset", params={ "pose_range": {"x": (-0.1, 0.1), "y": (-0.25, 0.25), "z": (0.0, 0.0)}, "velocity_range": {}, "asset_cfg": SceneEntityCfg("object", body_names="Object"), }, ) @configclass class RewardsCfg: """Reward terms for the MDP.""" reaching_object = RewTerm(func=mdp.object_ee_distance, params={"std": 0.1}, weight=1.0) lifting_object = RewTerm(func=mdp.object_is_lifted, params={"minimal_height": 0.06}, weight=15.0) object_goal_tracking = RewTerm( func=mdp.object_goal_distance, params={"std": 0.3, "minimal_height": 0.06, "command_name": "object_pose"}, weight=16.0, ) object_goal_tracking_fine_grained = RewTerm( func=mdp.object_goal_distance, params={"std": 0.05, "minimal_height": 0.06, "command_name": "object_pose"}, weight=5.0, ) # action penalty action_rate = RewTerm(func=mdp.action_rate_l2, weight=-1e-3) joint_vel = RewTerm( func=mdp.joint_vel_l2, weight=-1e-4, params={"asset_cfg": SceneEntityCfg("robot")}, ) @configclass class TerminationsCfg: """Termination terms for the MDP.""" time_out = DoneTerm(func=mdp.time_out, time_out=True) object_dropping = DoneTerm( func=mdp.base_height, params={"minimum_height": -0.05, "asset_cfg": SceneEntityCfg("object")} ) @configclass class CurriculumCfg: """Curriculum terms for the MDP.""" action_rate = CurrTerm( func=mdp.modify_reward_weight, params={"term_name": "action_rate", "weight": -1e-1, "num_steps": 10000} ) joint_vel = CurrTerm( func=mdp.modify_reward_weight, params={"term_name": "joint_vel", "weight": -1e-1, "num_steps": 10000} ) ## # Environment configuration ## @configclass class LiftEnvCfg(RLTaskEnvCfg): """Configuration for the lifting environment.""" # Scene settings scene: ObjectTableSceneCfg = ObjectTableSceneCfg(num_envs=4096, env_spacing=2.5) # Basic settings observations: ObservationsCfg = ObservationsCfg() actions: ActionsCfg = ActionsCfg() commands: CommandsCfg = CommandsCfg() # MDP settings rewards: RewardsCfg = RewardsCfg() terminations: TerminationsCfg = TerminationsCfg() events: EventCfg = EventCfg() curriculum: CurriculumCfg = CurriculumCfg() def __post_init__(self): """Post initialization.""" # general settings self.decimation = 2 self.episode_length_s = 5.0 # simulation settings self.sim.dt = 0.01 # 100Hz self.sim.physx.bounce_threshold_velocity = 0.2 self.sim.physx.bounce_threshold_velocity = 0.01 self.sim.physx.gpu_found_lost_aggregate_pairs_capacity = 1024 * 1024 * 4 self.sim.physx.gpu_total_aggregate_pairs_capacity = 16 * 1024 self.sim.physx.friction_correlation_distance = 0.00625

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/lift/mdp/rewards.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import torch from typing import TYPE_CHECKING from omni.isaac.orbit.assets import RigidObject from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.sensors import FrameTransformer from omni.isaac.orbit.utils.math import combine_frame_transforms if TYPE_CHECKING: from omni.isaac.orbit.envs import RLTaskEnv def object_is_lifted( env: RLTaskEnv, minimal_height: float, object_cfg: SceneEntityCfg = SceneEntityCfg("object") ) -> torch.Tensor: """Reward the agent for lifting the object above the minimal height.""" object: RigidObject = env.scene[object_cfg.name] return torch.where(object.data.root_pos_w[:, 2] > minimal_height, 1.0, 0.0) def object_ee_distance( env: RLTaskEnv, std: float, object_cfg: SceneEntityCfg = SceneEntityCfg("object"), ee_frame_cfg: SceneEntityCfg = SceneEntityCfg("ee_frame"), ) -> torch.Tensor: """Reward the agent for reaching the object using tanh-kernel.""" # extract the used quantities (to enable type-hinting) object: RigidObject = env.scene[object_cfg.name] ee_frame: FrameTransformer = env.scene[ee_frame_cfg.name] # Target object position: (num_envs, 3) cube_pos_w = object.data.root_pos_w # End-effector position: (num_envs, 3) ee_w = ee_frame.data.target_pos_w[..., 0, :] # Distance of the end-effector to the object: (num_envs,) object_ee_distance = torch.norm(cube_pos_w - ee_w, dim=1) return 1 - torch.tanh(object_ee_distance / std) def object_goal_distance( env: RLTaskEnv, std: float, minimal_height: float, command_name: str, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), object_cfg: SceneEntityCfg = SceneEntityCfg("object"), ) -> torch.Tensor: """Reward the agent for tracking the goal pose using tanh-kernel.""" # extract the used quantities (to enable type-hinting) robot: RigidObject = env.scene[robot_cfg.name] object: RigidObject = env.scene[object_cfg.name] command = env.command_manager.get_command(command_name) # compute the desired position in the world frame des_pos_b = command[:, :3] des_pos_w, _ = combine_frame_transforms(robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], des_pos_b) # distance of the end-effector to the object: (num_envs,) distance = torch.norm(des_pos_w - object.data.root_pos_w[:, :3], dim=1) # rewarded if the object is lifted above the threshold return (object.data.root_pos_w[:, 2] > minimal_height) * (1 - torch.tanh(distance / std))

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/lift/mdp/terminations.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """Common functions that can be used to activate certain terminations for the lift task. The functions can be passed to the :class:`omni.isaac.orbit.managers.TerminationTermCfg` object to enable the termination introduced by the function. """ from __future__ import annotations import torch from typing import TYPE_CHECKING from omni.isaac.orbit.assets import RigidObject from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.utils.math import combine_frame_transforms if TYPE_CHECKING: from omni.isaac.orbit.envs import RLTaskEnv def object_reached_goal( env: RLTaskEnv, command_name: str = "object_pose", threshold: float = 0.02, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), object_cfg: SceneEntityCfg = SceneEntityCfg("object"), ) -> torch.Tensor: """Termination condition for the object reaching the goal position. Args: env: The environment. command_name: The name of the command that is used to control the object. threshold: The threshold for the object to reach the goal position. Defaults to 0.02. robot_cfg: The robot configuration. Defaults to SceneEntityCfg("robot"). object_cfg: The object configuration. Defaults to SceneEntityCfg("object"). """ # extract the used quantities (to enable type-hinting) robot: RigidObject = env.scene[robot_cfg.name] object: RigidObject = env.scene[object_cfg.name] command = env.command_manager.get_command(command_name) # compute the desired position in the world frame des_pos_b = command[:, :3] des_pos_w, _ = combine_frame_transforms(robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], des_pos_b) # distance of the end-effector to the object: (num_envs,) distance = torch.norm(des_pos_w - object.data.root_pos_w[:, :3], dim=1) # rewarded if the object is lifted above the threshold return distance < threshold

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/lift/mdp/observations.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from __future__ import annotations import torch from typing import TYPE_CHECKING from omni.isaac.orbit.assets import RigidObject from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.utils.math import subtract_frame_transforms if TYPE_CHECKING: from omni.isaac.orbit.envs import RLTaskEnv def object_position_in_robot_root_frame( env: RLTaskEnv, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot"), object_cfg: SceneEntityCfg = SceneEntityCfg("object"), ) -> torch.Tensor: """The position of the object in the robot's root frame.""" robot: RigidObject = env.scene[robot_cfg.name] object: RigidObject = env.scene[object_cfg.name] object_pos_w = object.data.root_pos_w[:, :3] object_pos_b, _ = subtract_frame_transforms( robot.data.root_state_w[:, :3], robot.data.root_state_w[:, 3:7], object_pos_w ) return object_pos_b

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/lift/config/franka/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import gymnasium as gym import os from . import agents, ik_abs_env_cfg, ik_rel_env_cfg, joint_pos_env_cfg ## # Register Gym environments. ## ## # Joint Position Control ## gym.register( id="Isaac-Lift-Cube-Franka-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": joint_pos_env_cfg.FrankaCubeLiftEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.LiftCubePPORunnerCfg, "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, disable_env_checker=True, ) gym.register( id="Isaac-Lift-Cube-Franka-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": joint_pos_env_cfg.FrankaCubeLiftEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.LiftCubePPORunnerCfg, "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, disable_env_checker=True, ) ## # Inverse Kinematics - Absolute Pose Control ## gym.register( id="Isaac-Lift-Cube-Franka-IK-Abs-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_abs_env_cfg.FrankaCubeLiftEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.LiftCubePPORunnerCfg, "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, disable_env_checker=True, ) gym.register( id="Isaac-Lift-Cube-Franka-IK-Abs-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_abs_env_cfg.FrankaCubeLiftEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.LiftCubePPORunnerCfg, "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, disable_env_checker=True, ) ## # Inverse Kinematics - Relative Pose Control ## gym.register( id="Isaac-Lift-Cube-Franka-IK-Rel-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_rel_env_cfg.FrankaCubeLiftEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.LiftCubePPORunnerCfg, "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", "robomimic_bc_cfg_entry_point": os.path.join(agents.__path__[0], "robomimic/bc.json"), }, disable_env_checker=True, ) gym.register( id="Isaac-Lift-Cube-Franka-IK-Rel-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", kwargs={ "env_cfg_entry_point": ik_rel_env_cfg.FrankaCubeLiftEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.LiftCubePPORunnerCfg, "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml", }, disable_env_checker=True, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/lift/config/franka/joint_pos_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from omni.isaac.orbit.assets import RigidObjectCfg from omni.isaac.orbit.sensors import FrameTransformerCfg from omni.isaac.orbit.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg from omni.isaac.orbit.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg from omni.isaac.orbit.sim.spawners.from_files.from_files_cfg import UsdFileCfg from omni.isaac.orbit.utils import configclass from omni.isaac.orbit.utils.assets import ISAAC_NUCLEUS_DIR from omni.isaac.orbit_tasks.manipulation.lift import mdp from omni.isaac.orbit_tasks.manipulation.lift.lift_env_cfg import LiftEnvCfg ## # Pre-defined configs ## from omni.isaac.orbit.markers.config import FRAME_MARKER_CFG # isort: skip from omni.isaac.orbit_assets.franka import FRANKA_PANDA_CFG # isort: skip @configclass class FrankaCubeLiftEnvCfg(LiftEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # Set Franka as robot self.scene.robot = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") # Set actions for the specific robot type (franka) self.actions.body_joint_pos = mdp.JointPositionActionCfg( asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True ) self.actions.finger_joint_pos = mdp.BinaryJointPositionActionCfg( asset_name="robot", joint_names=["panda_finger.*"], open_command_expr={"panda_finger_.*": 0.04}, close_command_expr={"panda_finger_.*": 0.0}, ) # Set the body name for the end effector self.commands.object_pose.body_name = "panda_hand" # Set Cube as object self.scene.object = RigidObjectCfg( prim_path="{ENV_REGEX_NS}/Object", init_state=RigidObjectCfg.InitialStateCfg(pos=[0.5, 0, 0.055], rot=[1, 0, 0, 0]), spawn=UsdFileCfg( usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Blocks/DexCube/dex_cube_instanceable.usd", scale=(0.8, 0.8, 0.8), rigid_props=RigidBodyPropertiesCfg( solver_position_iteration_count=16, solver_velocity_iteration_count=1, max_angular_velocity=1000.0, max_linear_velocity=1000.0, max_depenetration_velocity=5.0, disable_gravity=False, ), ), ) # Listens to the required transforms marker_cfg = FRAME_MARKER_CFG.copy() marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) marker_cfg.prim_path = "/Visuals/FrameTransformer" self.scene.ee_frame = FrameTransformerCfg( prim_path="{ENV_REGEX_NS}/Robot/panda_link0", debug_vis=False, visualizer_cfg=marker_cfg, target_frames=[ FrameTransformerCfg.FrameCfg( prim_path="{ENV_REGEX_NS}/Robot/panda_hand", name="end_effector", offset=OffsetCfg( pos=[0.0, 0.0, 0.1034], ), ), ], ) @configclass class FrankaCubeLiftEnvCfg_PLAY(FrankaCubeLiftEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # make a smaller scene for play self.scene.num_envs = 50 self.scene.env_spacing = 2.5 # disable randomization for play self.observations.policy.enable_corruption = False

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go1/rough_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from omni.isaac.orbit.utils import configclass from omni.isaac.orbit_tasks.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg ## # Pre-defined configs ## from omni.isaac.orbit_assets.unitree import UNITREE_GO1_CFG # isort: skip @configclass class UnitreeGo1RoughEnvCfg(LocomotionVelocityRoughEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() self.scene.robot = UNITREE_GO1_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/trunk" # scale down the terrains because the robot is small self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_height_range = (0.025, 0.1) self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_range = (0.01, 0.06) self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_step = 0.01 # reduce action scale self.actions.joint_pos.scale = 0.25 # event self.events.push_robot = None self.events.add_base_mass.params["mass_range"] = (-1.0, 3.0) self.events.add_base_mass.params["asset_cfg"].body_names = "trunk" self.events.base_external_force_torque.params["asset_cfg"].body_names = "trunk" self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0) self.events.reset_base.params = { "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, "velocity_range": { "x": (0.0, 0.0), "y": (0.0, 0.0), "z": (0.0, 0.0), "roll": (0.0, 0.0), "pitch": (0.0, 0.0), "yaw": (0.0, 0.0), }, } # rewards self.rewards.feet_air_time.params["sensor_cfg"].body_names = ".*_foot" self.rewards.feet_air_time.weight = 0.01 self.rewards.undesired_contacts = None self.rewards.dof_torques_l2.weight = -0.0002 self.rewards.track_lin_vel_xy_exp.weight = 1.5 self.rewards.track_ang_vel_z_exp.weight = 0.75 self.rewards.dof_acc_l2.weight = -2.5e-7 # terminations self.terminations.base_contact.params["sensor_cfg"].body_names = "trunk" @configclass class UnitreeGo1RoughEnvCfg_PLAY(UnitreeGo1RoughEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # make a smaller scene for play self.scene.num_envs = 50 self.scene.env_spacing = 2.5 # spawn the robot randomly in the grid (instead of their terrain levels) self.scene.terrain.max_init_terrain_level = None # reduce the number of terrains to save memory if self.scene.terrain.terrain_generator is not None: self.scene.terrain.terrain_generator.num_rows = 5 self.scene.terrain.terrain_generator.num_cols = 5 self.scene.terrain.terrain_generator.curriculum = False # disable randomization for play self.observations.policy.enable_corruption = False # remove random pushing event self.events.base_external_force_torque = None self.events.push_robot = None

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go1/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import gymnasium as gym from . import agents, flat_env_cfg, rough_env_cfg ## # Register Gym environments. ## gym.register( id="Isaac-Velocity-Flat-Unitree-Go1-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.UnitreeGo1FlatEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo1FlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Flat-Unitree-Go1-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.UnitreeGo1FlatEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo1FlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Unitree-Go1-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.UnitreeGo1RoughEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo1RoughPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Unitree-Go1-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.UnitreeGo1RoughEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo1RoughPPORunnerCfg, }, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/cassie/rough_env_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from omni.isaac.orbit.managers import RewardTermCfg as RewTerm from omni.isaac.orbit.managers import SceneEntityCfg from omni.isaac.orbit.utils import configclass import omni.isaac.orbit_tasks.locomotion.velocity.mdp as mdp from omni.isaac.orbit_tasks.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg, RewardsCfg ## # Pre-defined configs ## from omni.isaac.orbit_assets.cassie import CASSIE_CFG # isort: skip @configclass class CassieRewardsCfg(RewardsCfg): termination_penalty = RewTerm(func=mdp.is_terminated, weight=-200.0) feet_air_time = RewTerm( func=mdp.feet_air_time_positive_biped, weight=2.5, params={ "sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*toe"), "command_name": "base_velocity", "threshold": 0.3, }, ) joint_deviation_hip = RewTerm( func=mdp.joint_deviation_l1, weight=-0.2, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["hip_abduction_.*", "hip_rotation_.*"])}, ) joint_deviation_toes = RewTerm( func=mdp.joint_deviation_l1, weight=-0.2, params={"asset_cfg": SceneEntityCfg("robot", joint_names=["toe_joint_.*"])}, ) # penalize toe joint limits dof_pos_limits = RewTerm( func=mdp.joint_pos_limits, weight=-1.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names="toe_joint_.*")}, ) @configclass class CassieRoughEnvCfg(LocomotionVelocityRoughEnvCfg): """Cassie rough environment configuration.""" rewards: CassieRewardsCfg = CassieRewardsCfg() def __post_init__(self): super().__post_init__() # scene self.scene.robot = CASSIE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/pelvis" # actions self.actions.joint_pos.scale = 0.5 # events self.events.push_robot = None self.events.add_base_mass = None self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0) self.events.base_external_force_torque.params["asset_cfg"].body_names = [".*pelvis"] self.events.reset_base.params = { "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)}, "velocity_range": { "x": (0.0, 0.0), "y": (0.0, 0.0), "z": (0.0, 0.0), "roll": (0.0, 0.0), "pitch": (0.0, 0.0), "yaw": (0.0, 0.0), }, } # terminations self.terminations.base_contact.params["sensor_cfg"].body_names = [".*pelvis"] # rewards self.rewards.undesired_contacts = None self.rewards.dof_torques_l2.weight = -5.0e-6 self.rewards.track_lin_vel_xy_exp.weight = 2.0 self.rewards.track_ang_vel_z_exp.weight = 1.0 self.rewards.action_rate_l2.weight *= 1.5 self.rewards.dof_acc_l2.weight *= 1.5 @configclass class CassieRoughEnvCfg_PLAY(CassieRoughEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # make a smaller scene for play self.scene.num_envs = 50 self.scene.env_spacing = 2.5 # spawn the robot randomly in the grid (instead of their terrain levels) self.scene.terrain.max_init_terrain_level = None # reduce the number of terrains to save memory if self.scene.terrain.terrain_generator is not None: self.scene.terrain.terrain_generator.num_rows = 5 self.scene.terrain.terrain_generator.num_cols = 5 self.scene.terrain.terrain_generator.curriculum = False self.commands.base_velocity.ranges.lin_vel_x = (0.7, 1.0) self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0) self.commands.base_velocity.ranges.heading = (0.0, 0.0) # disable randomization for play self.observations.policy.enable_corruption = False

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/cassie/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import gymnasium as gym from . import agents, flat_env_cfg, rough_env_cfg ## # Register Gym environments. ## gym.register( id="Isaac-Velocity-Flat-Cassie-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.CassieFlatEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CassieFlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Flat-Cassie-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.CassieFlatEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CassieFlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Cassie-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.CassieRoughEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CassieRoughPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Cassie-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.CassieRoughEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CassieRoughPPORunnerCfg, }, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_b/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import gymnasium as gym from . import agents, flat_env_cfg, rough_env_cfg ## # Register Gym environments. ## gym.register( id="Isaac-Velocity-Flat-Anymal-B-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.AnymalBFlatEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalBFlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Flat-Anymal-B-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.AnymalBFlatEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalBFlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Anymal-B-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.AnymalBRoughEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalBRoughPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Anymal-B-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.AnymalBRoughEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalBRoughPPORunnerCfg, }, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_c/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import gymnasium as gym from . import agents, flat_env_cfg, rough_env_cfg ## # Register Gym environments. ## gym.register( id="Isaac-Velocity-Flat-Anymal-C-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.AnymalCFlatEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalCFlatPPORunnerCfg, "skrl_cfg_entry_point": "omni.isaac.orbit_tasks.locomotion.velocity.anymal_c.agents:skrl_cfg.yaml", }, ) gym.register( id="Isaac-Velocity-Flat-Anymal-C-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.AnymalCFlatEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalCFlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Anymal-C-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.AnymalCRoughEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalCRoughPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Anymal-C-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.AnymalCRoughEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalCRoughPPORunnerCfg, }, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go2/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import gymnasium as gym from . import agents, flat_env_cfg, rough_env_cfg ## # Register Gym environments. ## gym.register( id="Isaac-Velocity-Flat-Unitree-Go2-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.UnitreeGo2FlatEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo2FlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Flat-Unitree-Go2-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.UnitreeGo2FlatEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo2FlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Unitree-Go2-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.UnitreeGo2RoughEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo2RoughPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Unitree-Go2-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.UnitreeGo2RoughEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo2RoughPPORunnerCfg, }, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_a1/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause import gymnasium as gym from . import agents, flat_env_cfg, rough_env_cfg ## # Register Gym environments. ## gym.register( id="Isaac-Velocity-Flat-Unitree-A1-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.UnitreeA1FlatEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeA1FlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Flat-Unitree-A1-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": flat_env_cfg.UnitreeA1FlatEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeA1FlatPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Unitree-A1-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.UnitreeA1RoughEnvCfg, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeA1RoughPPORunnerCfg, }, ) gym.register( id="Isaac-Velocity-Rough-Unitree-A1-Play-v0", entry_point="omni.isaac.orbit.envs:RLTaskEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": rough_env_cfg.UnitreeA1RoughEnvCfg_PLAY, "rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeA1RoughPPORunnerCfg, }, )

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/importer.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """Sub-module with utility for importing all modules in a package recursively.""" from __future__ import annotations import importlib import pkgutil import sys def import_packages(package_name: str, blacklist_pkgs: list[str] = None): """Import all sub-packages in a package recursively. It is easier to use this function to import all sub-packages in a package recursively than to manually import each sub-package. It replaces the need of the following code snippet on the top of each package's ``__init__.py`` file: .. code-block:: python import .locomotion.velocity import .manipulation.reach import .manipulation.lift Args: package_name: The package name. blacklist_pkgs: The list of blacklisted packages to skip. Defaults to None, which means no packages are blacklisted. """ # Default blacklist if blacklist_pkgs is None: blacklist_pkgs = [] # Import the package itself package = importlib.import_module(package_name) # Import all Python files for _ in _walk_packages(package.__path__, package.__name__ + ".", blacklist_pkgs=blacklist_pkgs): pass def _walk_packages( path: str | None = None, prefix: str = "", onerror: callable | None = None, blacklist_pkgs: list[str] | None = None, ): """Yields ModuleInfo for all modules recursively on path, or, if path is None, all accessible modules. Note: This function is a modified version of the original ``pkgutil.walk_packages`` function. It adds the `blacklist_pkgs` argument to skip blacklisted packages. Please refer to the original ``pkgutil.walk_packages`` function for more details. """ if blacklist_pkgs is None: blacklist_pkgs = [] def seen(p, m={}): if p in m: return True m[p] = True # noqa: R503 for info in pkgutil.iter_modules(path, prefix): # check blacklisted if any([black_pkg_name in info.name for black_pkg_name in blacklist_pkgs]): continue # yield the module info yield info if info.ispkg: try: __import__(info.name) except Exception: if onerror is not None: onerror(info.name) else: raise else: path = getattr(sys.modules[info.name], "__path__", None) or [] # don't traverse path items we've seen before path = [p for p in path if not seen(p)] yield from _walk_packages(path, info.name + ".", onerror, blacklist_pkgs)

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/__init__.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """Sub-package with utilities, data collectors and environment wrappers.""" from .importer import import_packages from .parse_cfg import get_checkpoint_path, load_cfg_from_registry, parse_env_cfg

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/parse_cfg.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """Sub-module with utilities for parsing and loading configurations.""" from __future__ import annotations import gymnasium as gym import importlib import inspect import os import re import yaml from omni.isaac.orbit.envs import RLTaskEnvCfg from omni.isaac.orbit.utils import update_class_from_dict, update_dict def load_cfg_from_registry(task_name: str, entry_point_key: str) -> dict | RLTaskEnvCfg: """Load default configuration given its entry point from the gym registry. This function loads the configuration object from the gym registry for the given task name. It supports both YAML and Python configuration files. It expects the configuration to be registered in the gym registry as: .. code-block:: python gym.register( id="My-Awesome-Task-v0", ... kwargs={"env_entry_point_cfg": "path.to.config:ConfigClass"}, ) The parsed configuration object for above example can be obtained as: .. code-block:: python from omni.isaac.orbit_tasks.utils.parse_cfg import load_cfg_from_registry cfg = load_cfg_from_registry("My-Awesome-Task-v0", "env_entry_point_cfg") Args: task_name: The name of the environment. entry_point_key: The entry point key to resolve the configuration file. Returns: The parsed configuration object. This is either a dictionary or a class object. Raises: ValueError: If the entry point key is not available in the gym registry for the task. """ # obtain the configuration entry point cfg_entry_point = gym.spec(task_name).kwargs.get(entry_point_key) # check if entry point exists if cfg_entry_point is None: raise ValueError( f"Could not find configuration for the environment: '{task_name}'." f" Please check that the gym registry has the entry point: '{entry_point_key}'." ) # parse the default config file if isinstance(cfg_entry_point, str) and cfg_entry_point.endswith(".yaml"): if os.path.exists(cfg_entry_point): # absolute path for the config file config_file = cfg_entry_point else: # resolve path to the module location mod_name, file_name = cfg_entry_point.split(":") mod_path = os.path.dirname(importlib.import_module(mod_name).__file__) # obtain the configuration file path config_file = os.path.join(mod_path, file_name) # load the configuration print(f"[INFO]: Parsing configuration from: {config_file}") with open(config_file, encoding="utf-8") as f: cfg = yaml.full_load(f) else: if callable(cfg_entry_point): # resolve path to the module location mod_path = inspect.getfile(cfg_entry_point) # load the configuration cfg_cls = cfg_entry_point() elif isinstance(cfg_entry_point, str): # resolve path to the module location mod_name, attr_name = cfg_entry_point.split(":") mod = importlib.import_module(mod_name) cfg_cls = getattr(mod, attr_name) else: cfg_cls = cfg_entry_point # load the configuration print(f"[INFO]: Parsing configuration from: {cfg_entry_point}") if callable(cfg_cls): cfg = cfg_cls() else: cfg = cfg_cls return cfg def parse_env_cfg( task_name: str, use_gpu: bool | None = None, num_envs: int | None = None, use_fabric: bool | None = None ) -> dict | RLTaskEnvCfg: """Parse configuration for an environment and override based on inputs. Args: task_name: The name of the environment. use_gpu: Whether to use GPU/CPU pipeline. Defaults to None, in which case it is left unchanged. num_envs: Number of environments to create. Defaults to None, in which case it is left unchanged. use_fabric: Whether to enable/disable fabric interface. If false, all read/write operations go through USD. This slows down the simulation but allows seeing the changes in the USD through the USD stage. Defaults to None, in which case it is left unchanged. Returns: The parsed configuration object. This is either a dictionary or a class object. Raises: ValueError: If the task name is not provided, i.e. None. """ # check if a task name is provided if task_name is None: raise ValueError("Please provide a valid task name. Hint: Use --task <task_name>.") # create a dictionary to update from args_cfg = {"sim": {"physx": dict()}, "scene": dict()} # resolve pipeline to use (based on input) if use_gpu is not None: if not use_gpu: args_cfg["sim"]["use_gpu_pipeline"] = False args_cfg["sim"]["physx"]["use_gpu"] = False args_cfg["sim"]["device"] = "cpu" else: args_cfg["sim"]["use_gpu_pipeline"] = True args_cfg["sim"]["physx"]["use_gpu"] = True args_cfg["sim"]["device"] = "cuda:0" # disable fabric to read/write through USD if use_fabric is not None: args_cfg["sim"]["use_fabric"] = use_fabric # number of environments if num_envs is not None: args_cfg["scene"]["num_envs"] = num_envs # load the default configuration cfg = load_cfg_from_registry(task_name, "env_cfg_entry_point") # update the main configuration if isinstance(cfg, dict): cfg = update_dict(cfg, args_cfg) else: update_class_from_dict(cfg, args_cfg) return cfg def get_checkpoint_path( log_path: str, run_dir: str = ".*", checkpoint: str = ".*", other_dirs: list[str] = None, sort_alpha: bool = True ) -> str: """Get path to the model checkpoint in input directory. The checkpoint file is resolved as: ``<log_path>/<run_dir>/<*other_dirs>/<checkpoint>``, where the :attr:`other_dirs` are intermediate folder names to concatenate. These cannot be regex expressions. If :attr:`run_dir` and :attr:`checkpoint` are regex expressions then the most recent (highest alphabetical order) run and checkpoint are selected. To disable this behavior, set the flag :attr:`sort_alpha` to False. Args: log_path: The log directory path to find models in. run_dir: The regex expression for the name of the directory containing the run. Defaults to the most recent directory created inside :attr:`log_path`. other_dirs: The intermediate directories between the run directory and the checkpoint file. Defaults to None, which implies that checkpoint file is directly under the run directory. checkpoint: The regex expression for the model checkpoint file. Defaults to the most recent torch-model saved in the :attr:`run_dir` directory. sort_alpha: Whether to sort the runs by alphabetical order. Defaults to True. If False, the folders in :attr:`run_dir` are sorted by the last modified time. Raises: ValueError: When no runs are found in the input directory. ValueError: When no checkpoints are found in the input directory. Returns: The path to the model checkpoint. Reference: https://github.com/leggedrobotics/legged_gym/blob/master/legged_gym/utils/helpers.py#L103 """ # check if runs present in directory try: # find all runs in the directory that math the regex expression runs = [ os.path.join(log_path, run) for run in os.scandir(log_path) if run.is_dir() and re.match(run_dir, run.name) ] # sort matched runs by alphabetical order (latest run should be last) if sort_alpha: runs.sort() else: runs = sorted(runs, key=os.path.getmtime) # create last run file path if other_dirs is not None: run_path = os.path.join(runs[-1], *other_dirs) else: run_path = runs[-1] except IndexError: raise ValueError(f"No runs present in the directory: '{log_path}' match: '{run_dir}'.") # list all model checkpoints in the directory model_checkpoints = [f for f in os.listdir(run_path) if re.match(checkpoint, f)] # check if any checkpoints are present if len(model_checkpoints) == 0: raise ValueError(f"No checkpoints in the directory: '{run_path}' match '{checkpoint}'.") # sort alphabetically while ensuring that *_10 comes after *_9 model_checkpoints.sort(key=lambda m: f"{m:0>15}") # get latest matched checkpoint file checkpoint_file = model_checkpoints[-1] return os.path.join(run_path, checkpoint_file)

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/wrappers/skrl.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """Wrapper to configure an :class:`RLTaskEnv` instance to skrl environment. The following example shows how to wrap an environment for skrl: .. code-block:: python from omni.isaac.orbit_tasks.utils.wrappers.skrl import SkrlVecEnvWrapper env = SkrlVecEnvWrapper(env) Or, equivalently, by directly calling the skrl library API as follows: .. code-block:: python from skrl.envs.torch.wrappers import wrap_env env = wrap_env(env, wrapper="isaac-orbit") """ from __future__ import annotations import copy import torch import tqdm from skrl.agents.torch import Agent from skrl.envs.wrappers.torch import Wrapper, wrap_env from skrl.resources.preprocessors.torch import RunningStandardScaler # noqa: F401 from skrl.resources.schedulers.torch import KLAdaptiveLR # noqa: F401 from skrl.trainers.torch import Trainer from skrl.trainers.torch.sequential import SEQUENTIAL_TRAINER_DEFAULT_CONFIG from skrl.utils.model_instantiators.torch import Shape # noqa: F401 from omni.isaac.orbit.envs import RLTaskEnv """ Configuration Parser. """ def process_skrl_cfg(cfg: dict) -> dict: """Convert simple YAML types to skrl classes/components. Args: cfg: A configuration dictionary. Returns: A dictionary containing the converted configuration. """ _direct_eval = [ "learning_rate_scheduler", "state_preprocessor", "value_preprocessor", "input_shape", "output_shape", ] def reward_shaper_function(scale): def reward_shaper(rewards, timestep, timesteps): return rewards * scale return reward_shaper def update_dict(d): for key, value in d.items(): if isinstance(value, dict): update_dict(value) else: if key in _direct_eval: d[key] = eval(value) elif key.endswith("_kwargs"): d[key] = value if value is not None else {} elif key in ["rewards_shaper_scale"]: d["rewards_shaper"] = reward_shaper_function(value) return d # parse agent configuration and convert to classes return update_dict(cfg) """ Vectorized environment wrapper. """ def SkrlVecEnvWrapper(env: RLTaskEnv): """Wraps around Orbit environment for skrl. This function wraps around the Orbit environment. Since the :class:`RLTaskEnv` environment wrapping functionality is defined within the skrl library itself, this implementation is maintained for compatibility with the structure of the extension that contains it. Internally it calls the :func:`wrap_env` from the skrl library API. Args: env: The environment to wrap around. Raises: ValueError: When the environment is not an instance of :class:`RLTaskEnv`. Reference: https://skrl.readthedocs.io/en/latest/modules/skrl.envs.wrapping.html """ # check that input is valid if not isinstance(env.unwrapped, RLTaskEnv): raise ValueError(f"The environment must be inherited from RLTaskEnv. Environment type: {type(env)}") # wrap and return the environment return wrap_env(env, wrapper="isaac-orbit") """ Custom trainer for skrl. """ class SkrlSequentialLogTrainer(Trainer): """Sequential trainer with logging of episode information. This trainer inherits from the :class:`skrl.trainers.base_class.Trainer` class. It is used to train agents in a sequential manner (i.e., one after the other in each interaction with the environment). It is most suitable for on-policy RL agents such as PPO, A2C, etc. It modifies the :class:`skrl.trainers.torch.sequential.SequentialTrainer` class with the following differences: * It also log episode information to the agent's logger. * It does not close the environment at the end of the training. Reference: https://skrl.readthedocs.io/en/latest/modules/skrl.trainers.base_class.html """ def __init__( self, env: Wrapper, agents: Agent | list[Agent], agents_scope: list[int] | None = None, cfg: dict | None = None, ): """Initializes the trainer. Args: env: Environment to train on. agents: Agents to train. agents_scope: Number of environments for each agent to train on. Defaults to None. cfg: Configuration dictionary. Defaults to None. """ # update the config _cfg = copy.deepcopy(SEQUENTIAL_TRAINER_DEFAULT_CONFIG) _cfg.update(cfg if cfg is not None else {}) # store agents scope agents_scope = agents_scope if agents_scope is not None else [] # initialize the base class super().__init__(env=env, agents=agents, agents_scope=agents_scope, cfg=_cfg) # init agents if self.env.num_agents > 1: for agent in self.agents: agent.init(trainer_cfg=self.cfg) else: self.agents.init(trainer_cfg=self.cfg) def train(self): """Train the agents sequentially. This method executes the training loop for the agents. It performs the following steps: * Pre-interaction: Perform any pre-interaction operations. * Compute actions: Compute the actions for the agents. * Step the environments: Step the environments with the computed actions. * Record the environments' transitions: Record the transitions from the environments. * Log custom environment data: Log custom environment data. * Post-interaction: Perform any post-interaction operations. * Reset the environments: Reset the environments if they are terminated or truncated. """ # init agent self.agents.init(trainer_cfg=self.cfg) self.agents.set_running_mode("train") # reset env states, infos = self.env.reset() # training loop for timestep in tqdm.tqdm(range(self.timesteps), disable=self.disable_progressbar): # pre-interaction self.agents.pre_interaction(timestep=timestep, timesteps=self.timesteps) # compute actions with torch.no_grad(): actions = self.agents.act(states, timestep=timestep, timesteps=self.timesteps)[0] # step the environments next_states, rewards, terminated, truncated, infos = self.env.step(actions) # note: here we do not call render scene since it is done in the env.step() method # record the environments' transitions with torch.no_grad(): self.agents.record_transition( states=states, actions=actions, rewards=rewards, next_states=next_states, terminated=terminated, truncated=truncated, infos=infos, timestep=timestep, timesteps=self.timesteps, ) # log custom environment data if "episode" in infos: for k, v in infos["episode"].items(): if isinstance(v, torch.Tensor) and v.numel() == 1: self.agents.track_data(f"EpisodeInfo / {k}", v.item()) # post-interaction self.agents.post_interaction(timestep=timestep, timesteps=self.timesteps) # reset the environments # note: here we do not call reset scene since it is done in the env.step() method # update states states.copy_(next_states) def eval(self) -> None: """Evaluate the agents sequentially. This method executes the following steps in loop: * Compute actions: Compute the actions for the agents. * Step the environments: Step the environments with the computed actions. * Record the environments' transitions: Record the transitions from the environments. * Log custom environment data: Log custom environment data. """ # set running mode if self.num_agents > 1: for agent in self.agents: agent.set_running_mode("eval") else: self.agents.set_running_mode("eval") # single agent if self.num_agents == 1: self.single_agent_eval() return # reset env states, infos = self.env.reset() # evaluation loop for timestep in tqdm.tqdm(range(self.initial_timestep, self.timesteps), disable=self.disable_progressbar): # compute actions with torch.no_grad(): actions = torch.vstack([ agent.act(states[scope[0] : scope[1]], timestep=timestep, timesteps=self.timesteps)[0] for agent, scope in zip(self.agents, self.agents_scope) ]) # step the environments next_states, rewards, terminated, truncated, infos = self.env.step(actions) with torch.no_grad(): # write data to TensorBoard for agent, scope in zip(self.agents, self.agents_scope): # track data agent.record_transition( states=states[scope[0] : scope[1]], actions=actions[scope[0] : scope[1]], rewards=rewards[scope[0] : scope[1]], next_states=next_states[scope[0] : scope[1]], terminated=terminated[scope[0] : scope[1]], truncated=truncated[scope[0] : scope[1]], infos=infos, timestep=timestep, timesteps=self.timesteps, ) # log custom environment data if "log" in infos: for k, v in infos["log"].items(): if isinstance(v, torch.Tensor) and v.numel() == 1: agent.track_data(k, v.item()) # perform post-interaction super(type(agent), agent).post_interaction(timestep=timestep, timesteps=self.timesteps) # reset environments # note: here we do not call reset scene since it is done in the env.step() method states.copy_(next_states)

Python

NVIDIA-Omniverse/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/wrappers/rl_games.py

# Copyright (c) 2022-2024, The ORBIT Project Developers. # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """Wrapper to configure an :class:`RLTaskEnv` instance to RL-Games vectorized environment. The following example shows how to wrap an environment for RL-Games and register the environment construction for RL-Games :class:`Runner` class: .. code-block:: python from rl_games.common import env_configurations, vecenv from omni.isaac.orbit_tasks.utils.wrappers.rl_games import RlGamesGpuEnv, RlGamesVecEnvWrapper # configuration parameters rl_device = "cuda:0" clip_obs = 10.0 clip_actions = 1.0 # wrap around environment for rl-games env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions) # register the environment to rl-games registry # note: in agents configuration: environment name must be "rlgpu" vecenv.register( "IsaacRlgWrapper", lambda config_name, num_actors, **kwargs: RlGamesGpuEnv(config_name, num_actors, **kwargs) ) env_configurations.register("rlgpu", {"vecenv_type": "IsaacRlgWrapper", "env_creator": lambda **kwargs: env}) """ from __future__ import annotations import gym.spaces # needed for rl-games incompatibility: https://github.com/Denys88/rl_games/issues/261 import gymnasium import torch from rl_games.common import env_configurations from rl_games.common.vecenv import IVecEnv from omni.isaac.orbit.envs import RLTaskEnv, VecEnvObs """ Vectorized environment wrapper. """ class RlGamesVecEnvWrapper(IVecEnv): """Wraps around Orbit environment for RL-Games. This class wraps around the Orbit environment. Since RL-Games works directly on GPU buffers, the wrapper handles moving of buffers from the simulation environment to the same device as the learning agent. Additionally, it performs clipping of observations and actions. For algorithms like asymmetric actor-critic, RL-Games expects a dictionary for observations. This dictionary contains "obs" and "states" which typically correspond to the actor and critic observations respectively. To use asymmetric actor-critic, the environment observations from :class:`RLTaskEnv` must have the key or group name "critic". The observation group is used to set the :attr:`num_states` (int) and :attr:`state_space` (:obj:`gym.spaces.Box`). These are used by the learning agent in RL-Games to allocate buffers in the trajectory memory. Since this is optional for some environments, the wrapper checks if these attributes exist. If they don't then the wrapper defaults to zero as number of privileged observations. .. caution:: This class must be the last wrapper in the wrapper chain. This is because the wrapper does not follow the :class:`gym.Wrapper` interface. Any subsequent wrappers will need to be modified to work with this wrapper. Reference: https://github.com/Denys88/rl_games/blob/master/rl_games/common/ivecenv.py https://github.com/NVIDIA-Omniverse/IsaacGymEnvs """ def __init__(self, env: RLTaskEnv, rl_device: str, clip_obs: float, clip_actions: float): """Initializes the wrapper instance. Args: env: The environment to wrap around. rl_device: The device on which agent computations are performed. clip_obs: The clipping value for observations. clip_actions: The clipping value for actions. Raises: ValueError: The environment is not inherited from :class:`RLTaskEnv`. ValueError: If specified, the privileged observations (critic) are not of type :obj:`gym.spaces.Box`. """ # check that input is valid if not isinstance(env.unwrapped, RLTaskEnv): raise ValueError(f"The environment must be inherited from RLTaskEnv. Environment type: {type(env)}") # initialize the wrapper self.env = env # store provided arguments self._rl_device = rl_device self._clip_obs = clip_obs self._clip_actions = clip_actions self._sim_device = env.unwrapped.device # information for privileged observations if self.state_space is None: self.rlg_num_states = 0 else: self.rlg_num_states = self.state_space.shape[0] def __str__(self): """Returns the wrapper name and the :attr:`env` representation string.""" return ( f"<{type(self).__name__}{self.env}>" f"\n\tObservations clipping: {self._clip_obs}" f"\n\tActions clipping : {self._clip_actions}" f"\n\tAgent device : {self._rl_device}" f"\n\tAsymmetric-learning : {self.rlg_num_states != 0}" ) def __repr__(self): """Returns the string representation of the wrapper.""" return str(self) """ Properties -- Gym.Wrapper """ @property def render_mode(self) -> str | None: """Returns the :attr:`Env` :attr:`render_mode`.""" return self.env.render_mode @property def observation_space(self) -> gym.spaces.Box: """Returns the :attr:`Env` :attr:`observation_space`.""" # note: rl-games only wants single observation space policy_obs_space = self.unwrapped.single_observation_space["policy"] if not isinstance(policy_obs_space, gymnasium.spaces.Box): raise NotImplementedError( f"The RL-Games wrapper does not currently support observation space: '{type(policy_obs_space)}'." f" If you need to support this, please modify the wrapper: {self.__class__.__name__}," " and if you are nice, please send a merge-request." ) # note: maybe should check if we are a sub-set of the actual space. don't do it right now since # in RLTaskEnv we are setting action space as (-inf, inf). return gym.spaces.Box(-self._clip_obs, self._clip_obs, policy_obs_space.shape) @property def action_space(self) -> gym.Space: """Returns the :attr:`Env` :attr:`action_space`.""" # note: rl-games only wants single action space action_space = self.unwrapped.single_action_space if not isinstance(action_space, gymnasium.spaces.Box): raise NotImplementedError( f"The RL-Games wrapper does not currently support action space: '{type(action_space)}'." f" If you need to support this, please modify the wrapper: {self.__class__.__name__}," " and if you are nice, please send a merge-request." ) # return casted space in gym.spaces.Box (OpenAI Gym) # note: maybe should check if we are a sub-set of the actual space. don't do it right now since # in RLTaskEnv we are setting action space as (-inf, inf). return gym.spaces.Box(-self._clip_actions, self._clip_actions, action_space.shape) @classmethod def class_name(cls) -> str: """Returns the class name of the wrapper.""" return cls.__name__ @property def unwrapped(self) -> RLTaskEnv: """Returns the base environment of the wrapper. This will be the bare :class:`gymnasium.Env` environment, underneath all layers of wrappers. """ return self.env.unwrapped """ Properties """ @property def num_envs(self) -> int: """Returns the number of sub-environment instances.""" return self.unwrapped.num_envs @property def device(self) -> str: """Returns the base environment simulation device.""" return self.unwrapped.device @property def state_space(self) -> gym.spaces.Box | None: """Returns the :attr:`Env` :attr:`observation_space`.""" # note: rl-games only wants single observation space critic_obs_space = self.unwrapped.single_observation_space.get("critic") # check if we even have a critic obs if critic_obs_space is None: return None elif not isinstance(critic_obs_space, gymnasium.spaces.Box): raise NotImplementedError( f"The RL-Games wrapper does not currently support state space: '{type(critic_obs_space)}'." f" If you need to support this, please modify the wrapper: {self.__class__.__name__}," " and if you are nice, please send a merge-request." ) # return casted space in gym.spaces.Box (OpenAI Gym) # note: maybe should check if we are a sub-set of the actual space. don't do it right now since # in RLTaskEnv we are setting action space as (-inf, inf). return gym.spaces.Box(-self._clip_obs, self._clip_obs, critic_obs_space.shape) def get_number_of_agents(self) -> int: """Returns number of actors in the environment.""" return getattr(self, "num_agents", 1) def get_env_info(self) -> dict: """Returns the Gym spaces for the environment.""" return { "observation_space": self.observation_space, "action_space": self.action_space, "state_space": self.state_space, } """ Operations - MDP """ def seed(self, seed: int = -1) -> int: # noqa: D102 return self.unwrapped.seed(seed) def reset(self): # noqa: D102 obs_dict, _ = self.env.reset() # process observations and states return self._process_obs(obs_dict) def step(self, actions): # noqa: D102 # move actions to sim-device actions = actions.detach().clone().to(device=self._sim_device) # clip the actions actions = torch.clamp(actions, -self._clip_actions, self._clip_actions) # perform environment step obs_dict, rew, terminated, truncated, extras = self.env.step(actions) # move time out information to the extras dict # this is only needed for infinite horizon tasks # note: only useful when `value_bootstrap` is True in the agent configuration if not self.unwrapped.cfg.is_finite_horizon: extras["time_outs"] = truncated.to(device=self._rl_device) # process observations and states obs_and_states = self._process_obs(obs_dict) # move buffers to rl-device # note: we perform clone to prevent issues when rl-device and sim-device are the same. rew = rew.to(device=self._rl_device) dones = (terminated | truncated).to(device=self._rl_device) extras = { k: v.to(device=self._rl_device, non_blocking=True) if hasattr(v, "to") else v for k, v in extras.items() } # remap extras from "log" to "episode" if "log" in extras: extras["episode"] = extras.pop("log") return obs_and_states, rew, dones, extras def close(self): # noqa: D102 return self.env.close() """ Helper functions """ def _process_obs(self, obs_dict: VecEnvObs) -> torch.Tensor | dict[str, torch.Tensor]: """Processing of the observations and states from the environment. Note: States typically refers to privileged observations for the critic function. It is typically used in asymmetric actor-critic algorithms. Args: obs_dict: The current observations from environment. Returns: If environment provides states, then a dictionary containing the observations and states is returned. Otherwise just the observations tensor is returned. """ # process policy obs obs = obs_dict["policy"] # clip the observations obs = torch.clamp(obs, -self._clip_obs, self._clip_obs) # move the buffer to rl-device obs = obs.to(device=self._rl_device).clone() # check if asymmetric actor-critic or not if self.rlg_num_states > 0: # acquire states from the environment if it exists try: states = obs_dict["critic"] except AttributeError: raise NotImplementedError("Environment does not define key 'critic' for privileged observations.") # clip the states states = torch.clamp(states, -self._clip_obs, self._clip_obs) # move buffers to rl-device states = states.to(self._rl_device).clone() # convert to dictionary return {"obs": obs, "states": states} else: return obs """ Environment Handler. """ class RlGamesGpuEnv(IVecEnv): """Thin wrapper to create instance of the environment to fit RL-Games runner.""" # TODO: Adding this for now but do we really need this? def __init__(self, config_name: str, num_actors: int, **kwargs): """Initialize the environment. Args: config_name: The name of the environment configuration. num_actors: The number of actors in the environment. This is not used in this wrapper. """ self.env: RlGamesVecEnvWrapper = env_configurations.configurations[config_name]["env_creator"](**kwargs) def step(self, action): # noqa: D102 return self.env.step(action) def reset(self): # noqa: D102 return self.env.reset() def get_number_of_agents(self) -> int: """Get number of agents in the environment. Returns: The number of agents in the environment. """ return self.env.get_number_of_agents() def get_env_info(self) -> dict: """Get the Gym spaces for the environment. Returns: The Gym spaces for the environment. """ return self.env.get_env_info()

Python