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InternRobotics/
InternVLA-N1

Name: InternVLA-N1
Rating: 2.5 (66 reviews)

vision-language-model

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InternVLA-N1: An Open Dual-System Navigation Foundation Model with Learned Latent Plans

Code

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.
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 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.

Acknowledgements

This repository is based on Qwen2.5-VL.

InternRobotics/InternVLA-N1