aDaikiKamata/patch_policy_libero10_tipsv2_lang

Model Card for patch_policy

This is a patch_policy policy trained with LeRobot.

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

See the full LeRobot documentation.

---

Model Details

• License: apache-2.0
• Robot type: panda
• Cameras: image, wrist_image

Inputs & Outputs

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

Inputs

Feature	Type	Shape
observation.images.image	VISUAL	(3, 256, 256)
observation.images.wrist_image	VISUAL	(3, 256, 256)
observation.state	STATE	(8,)

Outputs

Feature	Type	Shape
action	ACTION	(7,)

Training Dataset

• Repository: lerobot/libero_10_image
• Episodes: 379
• Frames: 101469
• Frame rate: 10 FPS
• Task(s): "put the white mug on the left plate and put the yellow and white mug on the right plate", "put the white mug on the plate and put the chocolate pudding to the right of the plate", "put the yellow and white mug in the microwave and close it", "turn on the stove and put the moka pot on it", "put both the alphabet soup and the cream cheese box in the basket", "put both the alphabet soup and the tomato sauce in the basket", "put both moka pots on the stove", "put both the cream cheese box and the butter in the basket", "put the black bowl in the bottom drawer of the cabinet and close it", "pick up the book and place it in the back compartment of the caddy"

Training Configuration

Setting	Value
Training steps	10000
Batch size	128
Optimizer	adamw
Learning rate	5e-05
Seed	1000
LeRobot version	0.6.2

---

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.

The short version to run and train this policy:

Run the policy on your robot

lerobot-rollout \
  --strategy.type=base \
  --robot.type=panda \
  --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=aDaikiKamata/patch_policy_libero10_tipsv2_lang \
  --task="put the white mug on the left plate and put the yellow and white mug on the right plate" \
  --duration=60

Replace the remaining <...> placeholders with your own values: --robot.port and the camera names/indices are specific to your machine, and the camera names must match the observation keys this policy was trained on.

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.

Train your own policy

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

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

---

Evaluation

No evaluation results have been provided for this policy yet.

---

Citation

If you use this policy, please cite the method linked in the description above, along with LeRobot:

@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}
}