Robotics
Transformers
Safetensors
mwm
feature-extraction
vision-language-model
manipulation
custom_code
Instructions to use InternRobotics/F1-VLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InternRobotics/F1-VLA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InternRobotics/F1-VLA", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| pipeline_tag: robotics | |
| library_name: transformers | |
| license: cc-by-nc-sa-4.0 | |
| tags: | |
| - vision-language-model | |
| - manipulation | |
| - robotics | |
| <div align="center"> | |
| <video src="https://cdn-uploads.huggingface.co/production/uploads/678123194248fde89e4fc9bf/_cbIWKHPzffRxIpfmqdFG.mp4" | |
| controls autoplay muted playsinline loop width="720"></video> | |
| <p><em>π Best viewed with sound on</em></p> | |
| </div> | |
| # F1: A Vision Language Action Model Bridging<br>Understanding and Generation to Actions | |
| [](https://arxiv.org/abs/2509.06951) | |
| [](https://github.com/InternRobotics/F1-VLA) | |
| [](https://aopolin-lv.github.io/F1-VLA) | |
| ## π Key Innovations | |
| - **π§ Predictive Inverse Dynamics**: Visual foresight generation for planning-based control | |
| - **ποΈ Mixture-of-Transformer**: Three specialized experts (Understanding, Generation, Action) | |
| - **π Three-Stage Training**: Progressive alignment, pretraining, and adaptation | |
| ## π€ Real-World Robot Experiments | |
| <!-- <div align="center"> | |
| <video src="https://cdn-uploads.huggingface.co/production/uploads/678123194248fde89e4fc9bf/FPZ45NJd9_B_T1gOP8QVf.qt" | |
| controls autoplay muted playsinline loop width="720"></video> | |
| <p><em>9 diverse manipulation tasks including pick-and-place, handover, and complex object manipulation</em></p> | |
| </div> --> | |
| <div style="display: flex; flex-direction: column; align-items: center; gap: 10px;"> | |
| <!-- First Row --> | |
| <div style="display: flex; justify-content: center; align-items: center; gap: 10px;"> | |
| <video controls autoplay loop muted width="200" style="border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);"> | |
| <source src="https://huggingface.co/spaces/Jia-Zeng/Robot_demos/resolve/main/arx_v2_long.mp4" type="video/mp4"> | |
| </video> | |
| <video controls autoplay loop muted width="200" style="border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);"> | |
| <source src="https://huggingface.co/spaces/Jia-Zeng/Robot_demos/resolve/main/arx_v1_dyna.mp4" type="video/mp4"> | |
| </video> | |
| <video controls autoplay loop muted width="210" style="border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);"> | |
| <source src="https://huggingface.co/spaces/Jia-Zeng/Robot_demos/resolve/main/franka_v1_sweep.mp4" type="video/mp4"> | |
| </video> | |
| </div> | |
| <!-- Second Row --> | |
| <div style="display: flex; justify-content: center; align-items: center; gap: 10px;"> | |
| <video controls autoplay loop muted width="200" style="border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);"> | |
| <source src="https://huggingface.co/spaces/Jia-Zeng/Robot_demos/resolve/main/genie_v2_handover.mp4" type="video/mp4"> | |
| </video> | |
| <video controls autoplay loop muted width="200" style="border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);"> | |
| <source src="https://huggingface.co/spaces/Jia-Zeng/Robot_demos/resolve/main/genie_v3_tea.mp4" type="video/mp4"> | |
| </video> | |
| <video controls autoplay loop muted width="210" style="border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);"> | |
| <source src="https://huggingface.co/spaces/Jia-Zeng/Robot_demos/resolve/main/genie_v1_flower.mp4" type="video/mp4"> | |
| </video> | |
| </div> | |
| <p><em>Diverse manipulation tasks across multiple robot platforms.</em></p> | |
| </div> | |
| ## π Performance Summary | |
| | Task | Platform | F1 | Ο0 | Improvement | | |
| |:--------:|:------------:|:------------------:|:------------:|:---------------:| | |
| | Multi-task | Genie-1 | 82.2% | 65.2% | +17.0% | | |
| | Adaptation | Franka | 66.7% | 53.3% | +13.4% | | |
| | Long-horizon | ARX LIFT II | 40.0% | 0.0% | +40.0% | | |
| | Dynamic Env | ARX LIFT II | 66.7% | 33.3% | +33.4% | | |
| ## Usage | |
| Please refer to our official repo [F1-VLA](https://github.com/InternRobotics/F1-VLA). | |
| ## π Citation | |
| If you find our work helpful, please cite: | |
| ```bibtex | |
| @article{f1_vla_2025, | |
| title={F1: A Vision Language Action Model Bridging Understanding and Generation to Actions}, | |
| author={Qi Lv and Weijie Kong and Hao Li and Jia Zeng and Zherui Qiu and Delin Qu and Haoming Song and Qizhi Chen and Xiang Deng and Jiangmiao Pang}, | |
| journal={Conference/Journal Name}, | |
| year={2025}, | |
| url={https://arxiv.org/abs/2509.06951} | |
| } | |
| ``` | |
| ## License | |
| This work is under the [cc-by-nc-sa-4.0](LICENSE). | |
| ## Acknowledgements | |
| This repository is based on [Lerobot](https://github.com/huggingface/lerobot), [Any4lerobot](https://github.com/Tavish9/any4lerobot/), and [VAR](https://github.com/FoundationVision/VAR). | |