ravediamond/turbovla_so101_pick_cups

Model Card for turbovla_so101

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

This is a turbovla_so101 policy trained with LeRobot.

<!-- 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/<hfuser>/<policyrepo_id>/resolve/main/demo.gif" width="60%"/> </p> -->

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: so_follower
• —Cameras: wrist_camera, front_top_camera

Inputs & Outputs

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

Inputs

Outputs

Training Dataset

• —Repository: ravediamond/pick_cups_all
• —Episodes: 180
• —Frames: 41342
• —Frame rate: 30 FPS
• —Task(s): "Pick up the blue cup", "Pick up the rose cup", "Pick the grey cup", "Pick up the grey cup"

<a class="flex" href="https://huggingface.co/spaces/lerobot/visualizedataset?path=ravediamond/pickcups_all"> <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

How to Get Started with the Model

New to LeRobot? These guides cover the full workflow:

• —[Install LeRobot](https://huggingface.co/docs/lerobot/main/en/installation) — set up the lerobot package.
• —[Hardware setup](https://huggingface.co/docs/lerobot/main/en/hardware_guide) — assemble, wire, and calibrate your robot and cameras.
• —[Record data & train a policy](https://huggingface.co/docs/lerobot/en/il_robots) — the end-to-end imitation-learning walkthrough.
• —[CLI cheat-sheet](https://huggingface.co/docs/lerobot/main/en/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=ravediamond/turbovla_so101_pick_cups \
  --task="Pick up the blue cup" \
  --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=${HF_USER}/<dataset> \
  --policy.type=turbovla_so101 \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<policy_repo_id> \
  --wandb.enable=true

Writes checkpoints to `outputs/train/<policyrepoid>/checkpoints/`.

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:

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.

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}
}