maximellerbach/lingbot_vla_v2_omx_multicubes_distill

Model Card for lingbot_vla_v2

LingBot-VLA 2.0 is a cross-embodiment Vision-Language-Action policy that pairs a Qwen3-VL-4B backbone with a sparse-MoE Qwen2 action expert, predicting action chunks via flow matching over a unified 55-D canonical state/action space.

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

Learn how to train and run it in the LeRobot lingbot_vla_v2 guide, or browse the full documentation.

---

Model Details

• License: [More Information Needed]
• Fine-tuned from: lerobot/lingbot_vla_v2_base
• Robot type: omx_follower
• Cameras: wrist, top

Inputs & Outputs

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

Inputs

Feature	Type	Shape
observation.state	STATE	(6,)
observation.images.camera_top	VISUAL	(3, 224, 224)
observation.images.camera_wrist_left	VISUAL	(3, 224, 224)
observation.images.camera_wrist_right	VISUAL	(3, 224, 224)

Outputs

Feature	Type	Shape
action	ACTION	(6,)

Training Dataset

• Repository: maximellerbach/omx_multicubes
• Episodes: 176
• Frames: 137392
• Frame rate: 30 FPS
• Task(s): "pick all the cubes and place them one by one in the blue square"

Training Configuration

Setting	Value
Training steps	10000
Batch size	4
Optimizer	adamw
Learning rate	5e-05
Seed	1000
LeRobot version	0.6.2

---

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=omx_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=maximellerbach/lingbot_vla_v2_omx_multicubes_distill \
  --task="pick all the cubes and place them one by one in the blue square" \
  --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

Fine-tune your own from the same base model, lerobot/lingbot_vla_v2_base:

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.path=lerobot/lingbot_vla_v2_base \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<policy_repo_id> \
  --wandb.enable=true

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

---

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:

@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}
}