Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation.
This policy has been trained and pushed to the Hub using
LeRobot.
The policy consumes these observation features and produces these action features.
1lerobot-rollout \
2 --strategy.type=base \
3 --robot.type=so_follower \
4 --robot.port=<your_robot_port> \
5 --robot.cameras="{ <camera_1>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}, <camera_2>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}}" \
6 --policy.path=eslab1234/grasp_C1_red_diffusion_h32_a8_ddim10_50k \
7 --task="grasp one block from pregrasp and lift" \
8 --duration=60
When
--strategy.type=base is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at
rollout documentation.
1lerobot-train \
2 --dataset.repo_id=${HF_USER}/<dataset> \
3 --policy.type=diffusion \
4 --output_dir=outputs/train/<policy_repo_id> \
5 --job_name=lerobot_training \
6 --policy.device=cuda \
7 --policy.repo_id=${HF_USER}/<policy_repo_id> \
8 --wandb.enable=true
If you use this policy, please cite the method linked in the description above, along with LeRobot:
1@misc{cadene2024lerobot,
2 author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
3 title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
4 howpublished = "\url{https://github.com/huggingface/lerobot}",
5 year = {2024}
6}
