Instructions to use SemyonXu616/VGAS-5-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use SemyonXu616/VGAS-5-shot 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=SemyonXu616/VGAS-5-shot \ --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=SemyonXu616/VGAS-5-shot - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| library_name: lerobot | |
| tags: | |
| - robotics | |
| - vision-language-action | |
| - libero | |
| - smolvla | |
| - vgas | |
| # VGAS and VGAS+ — 5-Shot LIBERO Checkpoints | |
| This repository contains the checkpoints used to evaluate | |
| [VGAS](https://arxiv.org/abs/2602.07399) and VGAS+ on the 5-shot LIBERO benchmark. | |
| The implementation is available in the | |
| [VGAS code repository](https://github.com/Jyugo-15/VGAS). | |
| ## Repository layout | |
| ```text | |
| smolvla/5_SHOT/pretrained_model/ # Shared 5-shot SmolVLA policy | |
| {suite}/vgas_critic/last.ckpt # VGAS inference-time critic | |
| {suite}/vgas_plus/pretrained_model/ # Distilled VGAS+ policy | |
| ``` | |
| Here, `{suite}` is one of `goal`, `object`, `spatial`, or `long` (`long` corresponds | |
| to `libero_10`). VGAS uses the shared SmolVLA policy together with the suite-specific | |
| critic for Best-of-N selection. The SFT policy is frozen while training the VGAS critic. | |
| VGAS+ directly executes the distilled policy and does not require a critic or | |
| inference-time reranking. | |
| ## VGAS+ checkpoints | |
| | Directory | LIBERO suite | | |
| |---|---| | |
| | `goal/vgas_plus/pretrained_model` | `libero_goal` | | |
| | `object/vgas_plus/pretrained_model` | `libero_object` | | |
| | `spatial/vgas_plus/pretrained_model` | `libero_spatial` | | |
| | `long/vgas_plus/pretrained_model` | `libero_10` | | |
| Download one policy with `huggingface_hub`: | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| snapshot_download( | |
| repo_id="SemyonXu616/VGAS-5-shot", | |
| allow_patterns=["goal/vgas_plus/pretrained_model/*"], | |
| local_dir="checkpoints/VGAS-5-shot", | |
| ) | |
| ``` | |
| The downloaded policy directory can be passed directly as `POLICY_PATH` to the | |
| VGAS+ evaluation scripts in the code repository. | |
| ## Citation | |
| ```bibtex | |
| @article{xu2026vgas, | |
| title = {VGAS: Value-Guided Action-Chunk Selection for Few-Shot Vision-Language-Action Adaptation}, | |
| author = {Xu, Changhua and Yu, En and Xuan, Junyu and Lu, Jie}, | |
| journal = {arXiv preprint arXiv:2602.07399}, | |
| year = {2026} | |
| } | |
| ``` | |
| The VGAS+ citation will be added when its preprint is available. | |