# Launch finetuning on your dataset
python lerobot/scripts/train.py \
--policy.path=dexforcecn/smolvla_robocasa_turn_on_microwave \
--dataset.repo_id=lerobot/svla_so101_pickplace \
--batch_size=64 \
--steps=20000 \
--output_dir=outputs/train/my_smolvla \
--job_name=my_smolvla_training \
--policy.device=cuda \
--wandb.enable=true# Run the policy using the record function
python -m lerobot.record \
--robot.type=so101_follower \
--robot.port=/dev/ttyACM0 \ # <- Use your port
--robot.id=my_blue_follower_arm \ # <- Use your robot id
--robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras
--dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording
--dataset.repo_id=HF_USER/dataset_name \ # <- This will be the dataset name on HF Hub
--dataset.episode_time_s=50 \
--dataset.num_episodes=10 \
--policy.path=dexforcecn/smolvla_robocasa_turn_on_microwaveSmolVLA RoboCasa TurnOnMicrowave
Fine-tuned SmolVLA checkpoint for the RoboCasa TurnOnMicrowave task.
Base model
lerobot/smolvla_robocasa
Training data
- RoboCasa LeRobot-format dataset for
TurnOnMicrowave
Inputs
observation.stateobservation.images.camera1observation.images.camera2observation.images.camera3
The training setup used the following camera rename map:
robot0_agentview_left -> camera1robot0_agentview_right -> camera2robot0_eye_in_hand -> camera3
Usage
Load with LeRobot:
lerobot-eval --policy.path=dexforcecn/smolvla_robocasa_turn_on_microwave ...
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# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details git clone https://github.com/huggingface/lerobot.git cd lerobot pip install -e .[smolvla]