InternNav – Navigation Toolbox

InternNav, an open-source toolbox for embodied navigation built on PyTorch, Habitat, and Isaac Sim, has been released. It supports modular navigation systems, multiple simulation platforms, and includes the InternVLA-N1 foundation model, which achieves state-of-the-art performance on navigation benchmarks. The toolbox offers comprehensive datasets, models, and benchmarks for vision-language navigation and visual navigation tasks.

InternNav is an All-in-one open-source toolbox for embodied navigation based on PyTorch, Habitat and Isaac Sim. - Modular Support of the Entire Navigation System We support modular customization and study of the entire navigation system, including vision-language navigation with discrete action space VLN-CE , visual navigation VN given point/image/trajectory goals, and the whole VLN system with continuous trajectory outputs. - Compatibility with Mainstream Simulation Platforms The toolbox is compatible with different training and evaluation requirements, supporting different environments for the usage of mainstream simulation platforms such as Habitat and Isaac Sim. - Comprehensive Datasets, Models and Benchmarks The toolbox supports the most comprehensive 6 datasets & benchmarks and 10+ popular baselines, including both mainstream and our established brand new ones. - State of the Art The toolbox supports the most advanced high-quality navigation dataset, InternData-N1, which includes 3k+ scenes and 830k VLN data covering diverse embodiments and scenes, and the first dual-system navigation foundation model with leading performance on all the benchmarks and zero-shot generalization capability in the real world, InternVLA-N1. | Time | Update | |---|---| | 2026/01 | InternNav v0.3.0 released. | | 2025/12 | We introduce Interactive Instance Goal Navigation IIGN and release VL-LN Bench to enable InternVLA-N1 to solve this task, with large-scale dialog-trajectory collection plus training and evaluation support. See | usage doc https://internrobotics.github.io/user guide/internnav/quick start/training.html is now available. This release provides two model configurations: InternVLA-N1 Dual System with NavDP and InternVLA-N1 Dual System DualVLN . For model architecture and training details, please refer to the DualVLN paper https://arxiv.org/abs/2512.08186 . inference-only demo /InternRobotics/InternNav/blob/main/scripts/notebooks/inference only demo.ipynb of InternVLA-N1. technical report https://internrobotics.github.io/internvla-n1.github.io/static/pdfs/InternVLA N1.pdf is released. Please check our homepage https://internrobotics.github.io/internvla-n1.github.io/ . files /InternRobotics/InternNav/blob/main/assets/3d printing files/go2 stand.STEP for Unitree Go2. official website https://internrobotics.shlab.org.cn/challenge/2025/ 🏠 Introduction -introduction 🔥 News -news 📚 Getting Started -getting-started 📦 Overview of Benchmark & Model Zoo -overview 🔧 Customization -customization 👥 Contribute -contribute 🚀 Community Deployment & Best Practices -community-deployment--best-practices 🔗 Citation -citation 📄 License -license 👏 Acknowledgements -acknowledgements Please refer to the documentation https://internrobotics.github.io/user guide/internnav/quick start/index.html for quick start with InternNav, from installation to training or evaluating supported models. VLN Benchmarks | VN Benchmarks | 🧠 VLN Single-System | 🎯 VN System System1 | 🤝 VLN Multi-System | | 📍 R2R Dataset | Model | Observation | NE ↓ | OS ↑ | SR ↑ | SPL ↑ | |---|---|---|---|---|---| | InternVLA-N1-wo-dagger S2 + | ShortestPathFollower https://aihabitat.org/docs/habitat-lab/habitat.tasks.nav.shortest path follower.ShortestPathFollower.html 4.05 70.7 64.3 58.5 📍 RxR Dataset | Model | Observation | NE ↓ | SR ↑ | SPL ↑ | nDTW ↑ | |---|---|---|---|---|---| | InternVLA-N1 S2 + | 4.58 61.4 51.8 70.0 📍 Flash Controller on R2R Unseen | Model | NE ↓ | OS ↑ | SR ↑ | SPL ↑ | |---|---|---|---|---| | Seq2Seq | 8.27 | 43.0 | 15.7 | 9.7 | | CMA | 7.52 | 45.0 | 24.4 | 18.2 | | RDP | 6.98 | 42.5 | 24.9 | 17.5 | | InternVLA-N1 System 2 + iPlanner | 4.91 | 55.53 | 47.07 | 41.09 | | InternVLA-N1 System 2 + NavDP | 4.22 | 67.33 | 58.72 | 50.98 | | InternVLA-N1 Dual System DualVLN | 3.90 | 69.93 | 63.62 | 56.49 | 📍 Physical Controller on R2R Unseen | Model | NE ↓ | OS ↑ | SR ↑ | SPL ↑ | |---|---|---|---|---| | Seq2Seq | 7.88 | 28.1 | 15.1 | 10.7 | | CMA | 7.26 | 31.4 | 22.1 | 18.6 | | RDP | 6.72 | 36.9 | 25.2 | 17.7 | | InternVLA-N1 Dual System DualVLN | 4.66 | 55.9 | 51.6 | 42.49 | 📍 ClutteredEnv Dataset | Model | SR ↑ | SPL ↑ | |---|---|---| | iPlanner | 84.8 | 83.6 | | ViPlanner | 72.4 | 72.3 | | NavDP <InternVLA-N1 System 1 | 89.8 | 87.7 | 📍 InternScenes Dataset | Model | SR ↑ | SPL ↑ | |---|---|---| | iPlanner | 48.8 | 46.7 | | ViPlanner | 54.3 | 52.5 | | NavDP <InternVLA-N1 System 1 | 65.7 | 60.7 | Please refer to the tutorial https://internrobotics.github.io/user guide/internnav/tutorials/index.html for advanced usage of InternNav, including customization of datasets, models and experimental settings. If you would like to contribute to InternNav, please check out our contribution guide /InternRobotics/InternNav/blob/main . For example, raising issues, fixing bugs in the framework, and adapting or adding new policies and data to the framework. Note: We welcome the feedback of the model's zero-shot performance when deploying in your own environment. Please show us your results and offer us your future demands regarding the model's capability. We will select the most valuable ones and collaborate with users together to solve them in the next few months : We are excited to see InternNav being deployed and extended by the community across different robots and real-world scenarios. Below are selected community-driven deployment guides and solution write-ups, which may serve as practical references for advanced users. - IROS Challenge Nav Track: Champion Solution 2025 A complete system-level solution and design analysis for Vision-and-Language Navigation in Physical Environments. 🔗 https://zhuanlan.zhihu.com/p/1969046543286907790 https://zhuanlan.zhihu.com/p/1969046543286907790 - Go2 Series Deployment Tutorial ShanghaiTech University Step-by-step edge deployment guide for InternNav-based perception and navigation. 🔗 https://github.com/cmjang/InternNav-deploy https://github.com/cmjang/InternNav-deploy - G1 Series Deployment Tutorial Wuhan University Detailed educational materials on vision-language navigation deployment. 🔗 Chapter 5: Vision-Language Navigation Part II If you find our InternVLA-N1 Dual System model helpful, please cite our ICLR paper and previous technical report: @inproceedings{ wei2026ground, title={Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-Language Navigation}, author={Meng Wei and Chenyang Wan and Jiaqi Peng and Xiqian Yu and Yuqiang Yang and Delin Feng and Wenzhe Cai and Chenming Zhu and Tai Wang and Jiangmiao Pang and Xihui Liu}, booktitle={The Fourteenth International Conference on Learning Representations}, year={2026}, url={https://openreview.net/forum?id=GK4rznYwhn} } @misc{internvla-n1, title = {{InternVLA-N1: An} Open Dual-System Navigation Foundation Model with Learned Latent Plans}, author = {InternNav Team}, year = {2025}, booktitle={arXiv}, } If you use this InternNav codebase to develop your method, please cite our codebase: @misc{internnav2025, title = {{InternNav: InternRobotics'} open platform for building generalized navigation foundation models}, author = {InternNav Contributors}, howpublished={\url{https://github.com/InternRobotics/InternNav}}, year = {2025} } If you use the specific pretrained models and benchmarks, please kindly cite the original papers below. @inproceedings{vlnpe, title={Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities}, author={Wang, Liuyi and Xia, Xinyuan and Zhao, Hui and Wang, Hanqing and Wang, Tai and Chen, Yilun and Liu, Chengju and Chen, Qijun and Pang, Jiangmiao}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision ICCV }, year={2025} } @inproceedings{streamvln, title = {StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling}, author = {Wei, Meng and Wan, Chenyang and Yu, Xiqian and Wang, Tai and Yang, Yuqiang and Mao, Xiaohan and Zhu, Chenming and Cai, Wenzhe and Wang, Hanqing and Chen, Yilun and Liu, Xihui and Pang, Jiangmiao}, booktitle={2026 IEEE International Conference on Robotics and Automation ICRA }, year = {2026} } @inproceedings{navdp, title = {NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance}, author = {Wenzhe Cai, Jiaqi Peng, Yuqiang Yang, Yujian Zhang, Meng Wei, Hanqing Wang, Yilun Chen, Tai Wang and Jiangmiao Pang}, booktitle={2026 IEEE International Conference on Robotics and Automation ICRA }, year = {2026}, } InternNav's codes are MIT licensed /InternRobotics/InternNav/blob/main/LICENSE . The open-sourced InternData-N1 data are under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/ . Other datasets like VLN-CE inherit their own distribution licenses. http://creativecommons.org/licenses/by-nc-sa/4.0/ InternUtopia https://github.com/InternRobotics/InternUtopia Previously GRUtopia : The closed-loop evaluation and GRScenes-100 data in this framework relies on the InternUtopia framework. Diffusion Policy https://github.com/real-stanford/diffusion policy : Diffusion policy implementation. LongCLIP https://github.com/beichenzbc/Long-CLIP : Long-text CLIP model. VLN-CE https://github.com/jacobkrantz/VLN-CE : Vision-and-Language Navigation in Continuous Environments based on Habitat. Qwen2.5-VL https://github.com/QwenLM/Qwen2.5-VL : The pretrained vision-language foundation model. LeRobot https://github.com/huggingface/lerobot : The data format used in this project largely follows the conventions of LeRobot.