README.md

---
pipeline_tag: robotics
library_name: transformers
license: cc-by-nc-sa-4.0
tags:
  - vision-language-model
  - video-language-model
  - navigation
---

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# 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).