# Launch finetuning on your dataset
python lerobot/scripts/train.py \
--policy.path=arrow-hf/smolvla-robotwin-place-bread-skillet-50ep \
--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=arrow-hf/smolvla-robotwin-place-bread-skillet-50ep
SmolVLA RoboTwin place_bread_skillet (50 ep, single instruction)
SmolVLA policy fine-tuned on 50 demonstration episodes of the place_bread_skillet task from RoboTwin 2.0 (demo_clean config), starting from the lerobot/smolvla_robotwin base checkpoint.
Task
Dual-arm pick-and-place: pick up the bread and place it inside the skillet.
- Robot: Agilex dual-arm, end-effector control (16D state, 16D action)
- Cameras: 3 RGB streams —
dual_cam_global,cam_wrist_65,cam_wrist_75 - Control rate: 10 Hz
- Single fixed instruction:
"place the bread in the skillet"(Strategy A, not random per-episode)
Training
| Config | Value |
|---|---|
| Base checkpoint | lerobot/smolvla_robotwin |
| Training data | 50 RoboTwin demonstrations (subset of place_bread_skillet_300ep), strategy A single instruction |
| Frames | 8,298 (~165 frames/ep) |
| Batch size | 32 |
| Steps | 6000 (~23 epochs) |
| Optimizer | AdamW, lr=1e-4 |
| Scheduler | Cosine, warmup=300, decay=6000 |
| Chunk size | 50 |
| Final train loss | 0.008 |
| Walltime | ~2h 15min (A100) |
Evaluation
Evaluated in RoboTwin 2.0 simulator (demo_clean config), 10 episodes, max_steps=400, action_chunk_exec=50.
| Model | Data | Base | Success |
|---|---|---|---|
SmolVLA (smolvla_base) |
300 ep | smolvla_base | 0/10 (0%) |
| SmolVLA (this model) | 50 ep | smolvla_robotwin | 6/10 (60%) |
X-VLA (xvla-base) |
300 ep | xvla-base | 8/10 (80%) |
Training with the smolvla_robotwin base checkpoint enables strong data efficiency: with 6× less data and 3× fewer training steps, this model jumps from 0% to 60% success rate on this task.
Successful episodes complete in 132–201 environment steps (13–20s); failed episodes time out at 400 steps.
Usage
from lerobot.policies.smolvla import SmolVLAPolicy
policy = SmolVLAPolicy.from_pretrained("arrow-hf/smolvla-robotwin-place-bread-skillet-50ep")
See LeRobot documentation for inference setup.
Citation
Built on SmolVLA and SmolVLA-RoboTwin, fine-tuned on data collected from RoboTwin 2.0.
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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]