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README.md

@@ -9,6 +9,8 @@ pipeline_tag: robotics
 Diffusion Policy (as per [Diffusion Policy: Visuomotor Policy
 Learning via Action Diffusion](https://arxiv.org/abs/2303.04137)) trained for the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht) with keypoint-only observations.
 
+Note: The original work trains keypoints-only with conditioning via inpainting. Here, we encode the observation along with the agent position and use the encoding as global conditioning for the denoising U-Net.
+
 ## How to Get Started with the Model
 
 Use `python lerobot/scripts/eval.py -p lerobot/diffusion_pusht` to evaluate for 50 episodes with the outputs sent to `outputs/eval`.
@@ -17,9 +19,9 @@ For further information, please see the [LeRobot library](https://github.com/hug
 
 ## Training Details
 
-Trained with TODO commit hash
+Trained with TODO commit hash one the PR is merged
 
-The model was trained using [LeRobot's training script](https://github.com/huggingface/lerobot/blob/d747195c5733c4f68d4bfbe62632d6fc1b605712/lerobot/scripts/train.py) and with the [pusht_keypoints](https://huggingface.co/datasets/lerobot/pusht_keypoints/tree/v1.5) dataset, using this command:
+The model was trained using [LeRobot's training script](TODO link with commit hash one the PR is merged) and with the [pusht_keypoints](https://huggingface.co/datasets/lerobot/pusht_keypoints/tree/v1.5) dataset, using this command:
 
 ```bash
 python lerobot/scripts/train.py \
@@ -55,7 +57,7 @@ The model was evaluated on the `PushT` environment from [gym-pusht](https://gith
 Here are the metrics for 500 episodes worth of evaluation.
 
 Metric|Average over 500 episodes
--|-|-
+-|-
 Average max. overlap ratio | 0.97
 Success rate (%) | 71.0