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VibeCuisine/
jetson1-act-peel-DAgger-iter1-adaptive-doris072026

Name: jetson1-act-peel-DAgger-iter1-adaptive-doris072026
Availability: InStock

VibeCuisine/cucumber-peel-DAgger-iter1-adaptive1-v1-trim

2304.13705

apache-2.0

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Model Card for act

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.

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

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

Model Details

License: apache-2.0
Robot type: vibeboard_follower_tilt
Cameras: top, wrist, base

Inputs & Outputs

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

Inputs

Feature	Type	Shape
`observation.state`	STATE	`(7,)`
`observation.images.top`	VISUAL	`(3, 480, 640)`
`observation.images.wrist`	VISUAL	`(3, 480, 640)`
`observation.images.base`	VISUAL	`(3, 480, 640)`

Outputs

Feature	Type	Shape
`action`	ACTION	`(7,)`

Training Dataset

Repository: VibeCuisine/cucumber-peel-DAgger-iter1-adaptive1-v1-trim
Episodes: 78
Frames: 28053
Frame rate: 20 FPS
Task(s): "peel the skin of the cucumber with several strokes, starting close to the blue gripper and moving vertically along the cucumber toward the robot origin"

Training Configuration

Setting	Value
Training steps	80000
Batch size	8
Optimizer	adamw
Learning rate	1e-05
Seed	42
LeRobot version	0.6.0

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

bash

lerobot-rollout \
  --strategy.type=base \
  --robot.type=vibeboard_follower_tilt \
  --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=VibeCuisine/jetson1-act-peel-DAgger-iter1-adaptive-doris072026 \
  --task="peel the skin of the cucumber with several strokes, starting close to the blue gripper and moving vertically along the cucumber toward the robot origin" \
  --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

bash

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

bibtex

@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}
}