vanniew/arducam_redcube_to_greybox_v2

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.

Demonstration Purpose:

• We train an ACT model to put a small red cube into a grey container of size 70x70x70.
• We use two cameras
  • One front facing camera of type Seeed Studio X10 USB wired camera.
  • One Gripper camera of type Arducam 1080p Low Light
  • Dataset is captured at frame size of 640x480
• The grey container is placed in a fixed position to the right of the SO-101 arm.
• We place the red cube into 6 discrete positions, all to the left of the SO_101 arm
• The base of the arm has to rotate approximately 0-30 degrees counterclockwise to cover all the positions.
• We record 10 episodes from each of the 6 discrete positions. The image below shows a picking position, we also marked all the 6 picking positions with a crayon on the table.

  

• We trained the model during 20000 training episodes.

Results

We test how well the model performs on the trained 6 discrete positions first and we see that 5 out of the 6 training positions work really well. We tested the policy on the predefined positions, 5 out of the 6 positions work well, the sixth failed. We also tried 2-3 picks in untrained positions but in the range covered by the pretrained points. We note that generalization is poor. Sometimes it works sometimes it doesn't.

The first two videos show a succsefull pick in two positions, the last video shows a pick that initially was unsuccesfull but the robot policy recovered gracefully.

Position 1 - Succesfull Atttempt

Position 2 - Succesfull Attempt

Position 3 - Policy succesfully recovered from failed attempt

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.

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Model Details

• License: apache-2.0
• Robot type: so_follower
• Cameras: gripper, top

Inputs & Outputs

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

Inputs

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

Outputs

Feature	Type	Shape
action	ACTION	(6,)

Training Dataset

• Repository: vanniew/arducam_redcube_to_greybox_v2_20260716_162714
• Episodes: 60
• Frames: 32233
• Frame rate: 30 FPS
• Task(s): "MAIN"

Training Configuration

Setting	Value
Training steps	20000
Batch size	16
Optimizer	adamw
Learning rate	1e-05
Seed	1000
LeRobot version	0.6.0

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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=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=vanniew/arducam_redcube_to_greybox_v2 \
  --task="MAIN" \
  --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=${HF_USER}/<dataset> \
  --policy.type=act \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<policy_repo_id> \
  --wandb.enable=true

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

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Evaluation

We noticed that it is very important to check the cameras before inferencing. The arducam 1080p low light suffered from overexposure. If this happened, we used guvcview to reset the camera to a usable setting before we started inferencing.

Task	Trials	Successes	Success rate
put the red cube into the grey container position 1	1	1	100%
put the red cube into the grey container position 2	1	0	0%
put the red cube into the grey container position 3	1	1	100%
put the red cube into the grey container position 4	1	1	100%
put the red cube into the grey container position 5	1	1	100%
put the red cube into the grey container position 6	1	1	100%
Total	6	5	83%

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}
}