--- title: InternVLA-N1 canonical_url: "https://www.modelscope.cn/models/InternRobotics/InternVLA-N1" md_url: "https://www.modelscope.cn/models/InternRobotics/InternVLA-N1.md" repository: InternRobotics/InternVLA-N1 last_updated: 2025-11-25 license: cc-by-nc-sa-4.0 pipeline_tag: robotics tasks: - robotics model_type: - qwen2_5_vl architectures: - InternVLAN1ForCausalLM parameters: 8.4B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - pytorch downloads: 649 stars: 3 tags: - vision-language-model - video-language-model - navigation --- # InternVLA-N1 > InternVLA-N1 - InternRobotics 在 ModelScope 开源的模型。InternVLA-N1: An Open Dual-System Navigation Foundation Model with Learned Latent Plans InternRobotics/InternVLA-N1 是 ModelScope 魔搭社区上的 8.4B 参数robotics模型，采用 cc-by-nc-sa-4.0 许可。 - **Repository**: InternRobotics/InternVLA-N1 - **License**: cc-by-nc-sa-4.0 - **Tasks**: robotics - **Parameters**: 8.4B - **Tags**: vision-language-model, video-language-model, navigation - **Downloads**: 649 - **Stars**: 3 - **Last updated**: 2025-11-25 Source: https://www.modelscope.cn/models/InternRobotics/InternVLA-N1 ---
# 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).