Instructions to use KrisYoung/SmolVLA-DexHand-Tactile-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use KrisYoung/SmolVLA-DexHand-Tactile-Checkpoints 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=KrisYoung/SmolVLA-DexHand-Tactile-Checkpoints \ --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=KrisYoung/SmolVLA-DexHand-Tactile-Checkpoints - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - robotics | |
| - vision-language-action | |
| - tactile | |
| - mujoco | |
| - smolvla | |
| library_name: lerobot | |
| # SmolVLA DexHand Tactile Checkpoints | |
| 本仓库存放 [SmolVLA DexHand Tactile](https://github.com/SCUT-Turing/SmolVLA_Dexhand_tactile) | |
| 项目的可复现实验权重。目标系统为 MuJoCo 中的 RM65 六轴机械臂与 Gaia 五指灵巧手。 | |
| ## 发布模型 | |
| - `apple_S2_tactile_regularized_residual_full_vision_300_best_step3000`:当前主模型 `S2V`, | |
| 普通 300 条数据、共享指尖 MLP、state-token 残差触觉、完整 SigLIP 视觉编码器参与训练。 | |
| 为了避免团队误用,本仓库只发布当前闭环成功率最高的代表权重;冻结视觉、局部解冻、 | |
| prefix-token、纠错数据、无触觉和 800 条数据消融 checkpoint 仍保留在项目归档中,不上传 Hub。 | |
| 模型结构不能只依据 checkpoint 内的 `config.json` 判断;触觉融合、视觉训练范围、数据来源、 | |
| SHA256 和闭环结果以代码仓库的 `configs/artifacts.json` 为准。 | |
| ## 使用 | |
| ```bash | |
| git clone git@github.com:SCUT-Turing/SmolVLA_Dexhand_tactile.git | |
| cd SmolVLA_Dexhand_tactile | |
| uv sync --locked --python 3.10 | |
| ./.venv/bin/python scripts/download_hf_artifacts.py \ | |
| --model-id apple_S2_tactile_regularized_residual_full_vision_300_best_step3000 | |
| HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 ./run infer S2V --scene-index 9 --viewer | |
| ``` | |
| 仓库已公开,无需 Hugging Face 登录。下载脚本使用 manifest 固定的 Hugging Face commit,避免 | |
| `main` 后续变化破坏复现。 | |
| ## 评测边界 | |
| 当前最好记录为 `S2V` 在同一份 30 场景开发集上的 17/30;这些场景已经参与 checkpoint | |
| 筛选,不能视为无偏最终测试。项目尚未验证真机下发,权重仅用于研究与 MuJoCo 验证。 | |
| ## 上游 | |
| 本项目基于 LeRobot SmolVLA 和 `HuggingFaceTB/SmolVLM2-500M-Video-Instruct`。使用时还需遵守 | |
| 上游模型、代码与资产各自的许可条件。 | |