Update model card for GN0 companion model

README.md

@@ -1,3 +1,113 @@
 ---
 license: cc-by-nc-4.0
+tags:
+- vision-language-navigation
+- embodied-ai
+- 3d-gaussian-splatting
+- navigation
+- gn0
+- gn-bench
+- bae
+pipeline_tag: robotics
 ---
+
+# GN-BAE
+
+GN-BAE is the companion navigation policy checkpoint for [GN0](https://github.com/TeleHuman/GN0), a unified research framework for **Generation**, **Evaluation**, and **Policy Learning** in Vision-and-Language Navigation (VLN).
+
+GN0 builds on 3D Gaussian Splatting (3DGS) to connect realistic scene construction, high-fidelity embodied simulation, and navigation policy evaluation in visually grounded indoor environments. In the GN0 ecosystem, GN-BAE serves as the navigation foundation model for map-based and map-free policy learning.
+
+## Intended Use
+
+This model is intended for research on embodied AI and Vision-and-Language Navigation, especially evaluation with the GN0 / GN-Bench workflow.
+
+Use this checkpoint with the GN0 codebase:
+
+- Code: https://github.com/TeleHuman/GN0
+- Project page: https://telehuman-gn0.github.io/
+- Paper: https://arxiv.org/abs/2606.03682
+
+## GN0 Context
+
+GN0 consists of three connected components:
+
+| Component | Description |
+| --- | --- |
+| GN-Matrix | Large-scale 3DGS navigation data with dynamic human avatars. |
+| GN-Bench | Interactive benchmark and simulator for high-fidelity VLN evaluation. |
+| GN-BAE | Navigation foundation model for map-based and map-free policy learning. |
+
+This repository hosts the GN-BAE checkpoint referenced by the GN0 model zoo.
+
+## Usage
+
+Please follow the installation and evaluation instructions in the GN0 repository.
+
+A typical GN0 workspace places this checkpoint under `model_zoo/bae`:
+
+```text
+GN0
+├── data
+│   ├── datasets
+│   │   └── GN_Matrix
+│   │       └── InteriorGS_episode
+│   └── scene_datasets
+│       └── InteriorGS
+├── GN-Bench-Tools
+├── model_zoo
+│   └── bae
+├── VLN_CE
+├── run.py
+└── eval_bae_InteriorGS.sh
+```
+
+Example InteriorGS evaluation command:
+
+```bash
+zsh eval_bae_InteriorGS.sh \
+  --model-path model_zoo/bae \
+  --chunks 1 \
+  --procs-per-gpu 1 \
+  --save-path tmp/bae_eval
+```
+
+Monitor evaluation progress:
+
+```bash
+watch -n 1 python analyze_results.py --path tmp/bae_eval
+```
+
+## Evaluation
+
+GN0 reports standard VLN metrics from JSON evaluation logs:
+
+| Metric | Meaning |
+| --- | --- |
+| TL | Average trajectory length |
+| NE ↓ | Navigation error |
+| OS ↑ | Oracle success |
+| SR ↑ | Success rate |
+| SPL ↑ | Success weighted by path length |
+
+Please refer to the GN0 repository for dataset preparation, simulator setup, and full evaluation details.
+
+## License
+
+This model is released under the Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0).
+
+## Citation
+
+If GN0, GN-Bench, or this model is useful for your research, please cite:
+
+```bibtex
+@article{li2026gn0,
+  title={GN0: Toward a Unified Paradigm for Generation, Evaluation, and Policy Learning in Visual-Language Navigation},
+  author={Li, Xinhai and Zhang, Xiaotao and Huang, Yuehao and Dong, Jiankun and Wang, Tianhang and Zhou, Sunyao and Wu, Yunzi and Sun, Chengnuo and Ge, Yunfei and Weng, Qizhen and Zhang, Chi and Bai, Chenjia and Li, Xuelong},
+  journal={arXiv preprint arXiv:2606.03682},
+  year={2026}
+}
+```
+
+## Acknowledgements
+
+GN-Bench-Tools is adapted from Habitat-Lab and customized for 3D Gaussian Splatting-based navigation. We thank the Habitat-Lab developers, the InteriorGS authors, and the broader Embodied AI and 3DGS open-source communities.