K-vr/cube_stack2_Pi0_absolute_RABC

Model Card for pi0

π₀ (Pi0) is a general-purpose robot foundation model from Physical Intelligence: a generalist Vision-Language-Action policy that understands visual inputs, interprets natural language instructions, and controls a variety of different robots across diverse tasks. The LeRobot implementation is adapted from their open-source OpenPI repository.

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

Learn how to train and run it in the LeRobot pi0 guide, or browse the full documentation.

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Model Details

• License: apache-2.0
• Fine-tuned from: lerobot/pi0_base
• Robot type: bi_so_follower_7dof
• Cameras: left_cam_left, left_cam_scene, right_cam_right, right_cam_scene_realsense

Inputs & Outputs

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

Inputs

Feature	Type	Shape
observation.images.base_0_rgb	VISUAL	(3, 224, 224)
observation.images.left_wrist_0_rgb	VISUAL	(3, 224, 224)
observation.images.right_wrist_0_rgb	VISUAL	(3, 224, 224)
observation.state	STATE	(32,)

Outputs

Feature	Type	Shape
action	ACTION	(14,)

Training Data Verification

Check	Value
Dataset repository	K-vr/cube_stack2_annotated
Resolved training root	/workspace/datasets/lerobot/cube_stack2_annotated
Repo/root folder-name check	✅ Match (cube_stack2_annotated)
Dataset size	60 episodes, 33599 frames at 30 FPS
Episodes selected	all
Task(s)	"Stack the three cubes with the 40 mm cube on the bottom, the 30 mm cube in the middle, and the 20 mm cube on top."

Training Configuration

Setting	Value
Training steps	4000
Batch size	24
Action representation	absolute
Sample weighting	RABC (head=sparse, kappa=0.08)
Sample-weight source	hf://datasets/K-vr/cube_stack2_annotated/sarm_progress.parquet

| Image augmentation | disabled | | Optimizer | adamw | | Learning rate | 2.5e-05 | | Seed | 1000 | | LeRobot version | 0.6.1 |

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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.

The short version to run and train this policy:

Run the policy on your robot

lerobot-rollout \
  --strategy.type=base \
  --robot.type=bi_so_follower_7dof \
  --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=K-vr/cube_stack2_Pi0_absolute_RABC \
  --task="Stack the three cubes with the 40 mm cube on the bottom, the 30 mm cube in the middle, and the 20 mm cube on top." \
  --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

This policy type is usually fine-tuned from the pretrained base model lerobot/pi0_base:

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.path=lerobot/pi0_base \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${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 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}
}