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.
Learn how to train and run it in the
LeRobot smolvla guide, or browse the
full documentation.
The policy consumes these observation features and produces these action features.
1lerobot-rollout \
2 --strategy.type=base \
3 --robot.type=so_follower \
4 --robot.port=<your_robot_port> \
5 --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}}" \
6 --policy.path=airhood/act_clean_table_lq_test_smolvla \
7 --task="clean_table_lq" \
8 --duration=60
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.
This policy type is usually fine-tuned from the pretrained base model
lerobot/smolvla_base:
1lerobot-train \
2 --dataset.repo_id=${HF_USER}/<dataset> \
3 --policy.path=lerobot/smolvla_base \
4 --output_dir=outputs/train/<policy_repo_id> \
5 --job_name=lerobot_training \
6 --policy.device=cuda \
7 --policy.repo_id=${HF_USER}/<policy_repo_id> \
8 --wandb.enable=true
If you use this policy, please cite the method linked in the description above, along with LeRobot:
1@misc{cadene2024lerobot,
2 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},
3 title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
4 howpublished = "\url{https://github.com/huggingface/lerobot}",
5 year = {2024}
6}
