Robotics
LeRobot
Safetensors
imitation-learning
x7s
smolvla
vision-language-action
offline-evaluation
Instructions to use Stevenshuqing/LW-Bench-SmolVLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use Stevenshuqing/LW-Bench-SmolVLA with LeRobot:
# 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]
# Launch finetuning on your dataset python lerobot/scripts/train.py \ --policy.path=Stevenshuqing/LW-Bench-SmolVLA \ --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=Stevenshuqing/LW-Bench-SmolVLA - Notebooks
- Google Colab
- Kaggle
| library_name: lerobot | |
| pipeline_tag: robotics | |
| tags: | |
| - lerobot | |
| - robotics | |
| - imitation-learning | |
| - x7s | |
| - smolvla | |
| - vision-language-action | |
| - offline-evaluation | |
| datasets: | |
| - LightwheelAI/Lightwheel-Tasks-X7S | |
| base_model: | |
| - lerobot/smolvla_base | |
| # LW-Bench SmolVLA 5K for X7s | |
| LeRobot SmolVLA policy fine-tuned from `lerobot/smolvla_base` on the audited X7s strict compositional split in LW-Compositional-Bench. | |
| ## Training contract | |
| - Dataset: `LightwheelAI/Lightwheel-Tasks-X7S` | |
| - Train split: 91 clean tasks; tasks 7, 119, and 149 held out | |
| - Inputs: three RGB cameras, 25D robot state, and canonical task language | |
| - Output: native 21D X7s absolute action | |
| - Hardware: 4 GPUs | |
| - Seed: 1000 | |
| - Effective global batch: 64 | |
| - Checkpoint: 5,000 global optimizer updates | |
| - Evaluation horizon: first 16 valid action steps | |
| ## Offline 5K results | |
| | Metric | Value | | |
| | --- | ---: | | |
| | H16 MAE | 0.117617 | | |
| | H16 RMSE | 0.227890 | | |
| | Gripper balanced accuracy | 87.08% | | |
| | Boundary-proxy MAE | 0.147732 | | |
| | Interior MAE | 0.111848 | | |
| | Boundary / interior MAE | 1.321x | | |
| These results cover three strict held-out tasks and are descriptive. Offline action error does not replace closed-loop success evaluation. | |
| ## Load with LeRobot | |
| ```python | |
| from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy | |
| policy = SmolVLAPolicy.from_pretrained("Stevenshuqing/LW-Bench-SmolVLA") | |
| policy.eval() | |
| ``` | |
| Use LeRobot 0.4.3 and the included preprocessor/postprocessor files. The repository contains the complete `pretrained_model` directory produced by LeRobot. | |
| ## Provenance | |
| - Base model: https://huggingface.co/lerobot/smolvla_base | |
| - Code and protocol: https://github.com/stevenqing/LW-Compositional-Bench | |
| - Full report: https://github.com/stevenqing/LW-Compositional-Bench/blob/main/artifacts/model-eval/x7s/rotation-v0/budget-5k/report.md | |
| - Code revision: `2d0e26dd2fe2e91046f416bd65bc369f8568d7f0` | |
| - `model.safetensors` SHA256: `f70959474aad109134f799e49dd313abf209380c8e1c808d8650e6d92287e242` | |
| ## Limitations | |
| This checkpoint is specific to the X7s observation/action schema. The benchmark is offline because the available A100 GPUs cannot provide Isaac Sim RT camera rendering. Transition boundaries are action-space proxies based on gripper sign changes and robust continuous-action peaks, not human semantic annotations. | |