Merge branch 'main' of https://huggingface.co/InternRobotics/InternVLA-N1 into main

README.md

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-license: cc-by-nc-sa-4.0
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+---
+pipeline_tag: robotics
+library_name: transformers
+license: cc-by-nc-sa-4.0
+tags:
+  - vision-language-model
+  - video-language-model
+  - navigation
+---
+
+<div id="top" align="center">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/64e6d9d229a548f66aff6e5b/4ZRvK6ySWCFj9mlpND791.gif" width=60% >
+
+</div>
+
+
+
+
+# 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)
+
+Project page: https://internrobotics.github.io/internvla-n1.github.io/
+
+Technical report: https://internrobotics.github.io/internvla-n1.github.io/static/pdfs/InternVLA_N1.pdf
+
+Data: https://huggingface.co/datasets/InternRobotics/InternData-N1
+
+
+
+## 🔔 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
+
+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).