smolvla_async

Model Card for smolvla

This smolvla policy is a ready-to-use example from the tutorial paper Robot Learning: A Tutorial.

Project Page: https://huggingface.co/spaces/lerobot/robot-learning-tutorial Code: https://github.com/fracapuano/robot-learning-tutorial

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.

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How to Get Started with the Model

For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:

Train from scratch

python -m lerobot.scripts.train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=act \
  --output_dir=outputs/train/<desired_policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
  --wandb.enable=true

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

Evaluate the policy/run inference

python -m lerobot.record \
  --robot.type=so100_follower \
  --dataset.repo_id=<hf_user>/eval_<dataset> \
  --policy.path=<hf_user>/<desired_policy_repo_id> \
  --episodes=10

Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.

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Model Details

• Model License: apache-2.0
• Related Tutorial Paper: Robot Learning: A Tutorial
• Tutorial Project Page: https://huggingface.co/spaces/lerobot/robot-learning-tutorial
• Tutorial Code: https://github.com/fracapuano/robot-learning-tutorial
• Tutorial Content License: The written content of the tutorial is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
• Tutorial Code Examples License: Source code examples in the tutorial's snippets/ directory are licensed under the MIT License.