act_bayonett_policy

Bayonet Lock — Imitation Learning with SO-101

Trained an SO-101 robot arm to perform a bayonet lock — pick up a peg, insert it into a fixed socket, and twist to lock. ~50 teleoperated episodes, 3 cameras, trained with ACT from scratch.

Success rate: ~50% under training conditions. Fails mostly in the twist phase.

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The Task

• Bayonet lock: insert peg into socket, rotate 70° to lock
• Two versions designed and printed, circular first, then rectangular
• Rectangular profile doesn't slip during the twist — made the task tractable
• Socket glued to a taped reference frame on the table
• Peg always starts from the same fixed position

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Camera Setup

Three cameras, three different jobs:

• Tried OAK-D Pro and Raspberry Pi Camera Module 3 first, integration issues and brutal latency
• Ended up with two Innomaker HD USB cameras and DJI Osmo Action 6 in webcam mode
• Resolution: 640×480 @ 30fps & 1280x720 @ 30fps, dropped from higher resolution to keep the pipeline lag-free
• Camera placement turned out to matter more than camera hardware

What I Learned

• Camera placement is the most important design decision — not model size, not episode count
• Data consistency during recording matters more than volume for contact tasks
• Rectangular geometry > circular for pick-and-twist — a small design change with a big effect on success
• The dataset format is model-agnostic — same 51 episodes trained both ACT and SmolVLA
• Knowing the exact failure mode is useful — it tells you exactly what to improve next

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Model Card for act

<!-- Provide a quick summary of what the model is/does. -->

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.

<!-- A short demo is worth more than any description! Record a GIF/video of the policy running on your robot, upload it to this repo, and embed it here: <p align="center"> <img src="https://huggingface.co/<hf_user>/<policy_repo_id>/resolve/main/demo.gif" width="60%"/> </p> -->

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: front_global, gripper, front_local

Inputs & Outputs

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

Inputs

Outputs

Training Dataset

• Repository: andrii4k/bayonett_lock_dataset_20260620_135402
• Episodes: 51
• Frames: 33930
• Frame rate: 30 FPS
• Task(s): "Insert the peg into the socket and twist to lock"

<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=andrii4k/bayonett_lock_dataset_20260620_135402"> <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/> </a>

Training Configuration

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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=andrii4k/act_bayonett_policy \
  --task="Insert the peg into the socket and twist to lock" \
  --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

<!-- Report real-robot results here: run the policy several times per task and count the successes. Delete the "No evaluation results" line and fill in this table instead: | Task | Trials | Successes | Success rate | | ---- | ------ | --------- | ------------ | | pick the lego brick | 10 | 8 | 80% | Also worth noting: anything that affects difficulty (new object positions, lighting, distractors, a different robot of the same type, ...). -->

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}
}