| --- |
| 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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| </div> |
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| # InternVLA-N1: An Open Dual-System Navigation Foundation Model with Learned Latent Plans |
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| [](https://github.com/InternRobotics/InternNav) |
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| Project page: https://internrobotics.github.io/internvla-n1.github.io/ |
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| Technical report: https://internrobotics.github.io/internvla-n1.github.io/static/pdfs/InternVLA_N1.pdf |
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| Data: https://huggingface.co/datasets/InternRobotics/InternData-N1 |
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| |
| ## 🔔 Important Notice |
| |
| * This repository hosts the **official release** of **InternVLA-N1**. |
| * The previously **InternVLA-N1** model has been renamed to **InternVLA-N1-Preview**. If you are looking for the **earlier preview version**, please check [InternVLA-N1-Preview](https://huggingface.co/InternRobotics/InternVLA-N1-Preview). |
| * We recommend using this official release for research and deployment, as it contains the most stable and up-to-date improvements. |
| |
| ### Key Difference: Preview vs Official |
| | Feature | InternVLA-N1-Preview | InternVLA-N1 (official) | |
| | ------------- | ----------------------------------------- | ------------------------------------------------------------------------ | |
| | System Design | Dual-System (synchronous) | Dual-System (asynchronous) | |
| | Training | System 1 trained only at System 2 inferrence step | System 1 trained on denser step (~25 cm), using latest System 2 hidden state | |
| | Inference | System 1, 2 infered at same frequency (~2 hz) | System 1, 2 infered asynchronously, allowing dynamic obstacle avoidance | |
| | Performance | Solid baseline in simulation & benchmarks | Improved smoothness, efficiency, and real-world zero-shot generalization | |
| | Status | Historical preview | Stable official release (recommended) |
| |
| ## Highlights |
| |
| - Dual-System Framework |
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| 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 |
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| 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 |
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| 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 |
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| 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). |
