Eshwar-2123/diffusion_pick_place_clean

Model Card for Diffusion Policy

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion is an imitation learning policy that models robot actions as a denoising diffusion process. It is particularly effective for learning multi-modal manipulation behaviors from demonstrations while producing smooth action trajectories.

This policy has been trained and pushed to the Hub using LeRobot.

Learn how to train and run it in the LeRobot Diffusion Policy guide, or browse the full documentation.

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Model Details

• License: apache-2.0
• Robot type: so_follower
• Cameras: front, wrist

Inputs & Outputs

The policy consumes these observation features and produces these action features.

Inputs

Feature	Type	Shape
observation.state	STATE	(6,)
observation.images.front	VISUAL	(3, 480, 640)
observation.images.wrist	VISUAL	(3, 480, 640)

Outputs

Feature	Type	Shape
action	ACTION	(6,)

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Training Dataset

• Repository: Eshwar-2123/pick_place_blue_cube_box_clean_v1_20260706_125359
• Episodes: 47
• Frames: 20321
• Frame rate: 30 FPS
• Task(s): ""

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Training Configuration

Setting	Value
Training steps	30000
Batch size	16
Optimizer	adamw
Learning rate	1e-04
Seed	1000
LeRobot version	0.5.2

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How to Get Started with the Model

New to LeRobot? These guides cover the full workflow:

• Install LeRobot — set up the lerobot package.
• Hardware setup — assemble, wire, and calibrate your robot and cameras.
• Record data & train a policy — the end-to-end imitation-learning walkthrough.
• CLI cheat-sheet — quick reference for the lerobot-* commands.

Run the policy on your robot

lerobot-rollout \
  --strategy.type=base \
  --robot.type=so_follower \
  --robot.port=<your_robot_port> \
  --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}}" \
  --policy.path=Eshwar-2123/diffusion_pick_place_clean \
  --task="" \
  --duration=60

Replace the remaining <...> placeholders with your own values.

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Train your own policy

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=diffusion \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<policy_repo_id> \
  --wandb.enable=true

Writes checkpoints to outputs/train/<policy_repo_id>/checkpoints/.

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Evaluation

No evaluation results have been provided for this policy yet.

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Citation

If you use this policy, please cite Diffusion Policy, along with LeRobot.

@article{chi2023diffusionpolicy,
  title={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
  author={Chi, Cheng and Feng, Siyuan and Du, Yilun and Xu, Zheng and Cousineau, Eric and Burchfiel, Benjamin and Song, Shuran},
  journal={RSS},
  year={2023}
}

@misc{cadene2024lerobot,
    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},
    title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
    howpublished = "\url{https://github.com/huggingface/lerobot}",
    year = {2024}
}