--- pipeline_tag: robotics library_name: transformers license: cc-by-nc-sa-4.0 tags: - vision-language-model - video-language-model - navigation ---
# InternVLA-N1: An Open Dual-System Navigation Foundation Model with Learned Latent Plans [![Code](https://img.shields.io/badge/GitHub-Code-181717?logo=github)](https://github.com/InternRobotics/InternNav) The technical report will be public in the coming open-source week. Please stay tuned! ## ⚠️ Important Notice * This repository was previously named **InternVLA-N1**, but is now renamed to **InternVLA-N1-Preview**. * The **official and latest release** is available at 👉 [InternVLA-N1](https://huggingface.co/InternRobotics/InternVLA-N1). * We recommend using the official release for new research and deployment, while this preview version is kept for **reproducibility and reference**. ## Highlights - Dual-System Framework The first navigation foundation model that achieves joint-tuning and asychronous inference of System-2 reasoning and System-1 action, resulting in smooth and efficient execution during the instruction-followed navigation procedure. - State-of-the-art The whole navigation foundation model with each system achieves state-of-the-art performance on both mainstream and our new established challenging benchmarks, including VLN-CE R2R & RxR, GRScenes-100, VLN-PE, etc. - Sim2Real Zero-shot Generalization The training is based on simulation data InternData-N1 only, with diverse scenes, embodiments and other randomization, while achieving great zero-shot generalization capabilities in the real world. ## Usage Please refer to [InternNav](https://github.com/InternRobotics/InternNav) for its inference, evaluation and gradio demo. ## Citation If you find our work helpful, please consider starring this repo 🌟 and cite: ```bibtex @misc{internvla-n1, title = {{InternVLA-N1: An} Open Dual-System Navigation Foundation Model with Learned Latent Plans}, author = {InternVLA-N1 Team}, year = {2025}, booktitle={arXiv}, } ``` ## License This work is under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/). ## Acknowledgements This repository is based on [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL).