---
license: apache-2.0
pipeline_tag: robotics
library_name: transformers
base_model: Qwen/Qwen2.5-VL-7B-Instruct
datasets:
- Chrono666/Nav-R2-OVON-CoT-Dataset
tags:
- robotics
- navigation
- object-goal-navigation
- vision-language-model
- qwen
---

# Nav-$R^2$: Dual-Relation Reasoning for Generalizable Open-Vocabulary Object-Goal Navigation

This repository contains the official implementation of the paper [Nav-$R^2$: Dual-Relation Reasoning for Generalizable Open-Vocabulary Object-Goal Navigation](https://huggingface.co/papers/2512.02400).

Object-goal navigation in open-vocabulary settings requires agents to locate novel objects in unseen environments. Nav-$R^2$ proposes a framework that explicitly models target-environment and environment-action relationships through structured Chain-of-Thought (CoT) reasoning and a Similarity-Aware Memory. This approach enables state-of-the-art performance in localizing unseen objects efficiently while maintaining real-time inference.

For more details on the code, installation, training, and evaluation, please refer to the [GitHub repository](https://github.com/AMAP-EAI/Nav-R2).

## Overview

<p align="center">
 <img src="https://github.com/AMAP-EAI/Nav-R2/raw/main/figs/title.png" width="100%">
</p>
<p align="center">
 <img src="https://github.com/AMAP-EAI/Nav-R2/raw/main/figs/teaser.png" width="100%">
</p>

### Pipeline and Structure
<p align="center">
 <img src="https://github.com/AMAP-EAI/Nav-R2/raw/main/figs/pipeline.png" width="100%">
</p>

### Results on OVON
Here shows the results on OVON dataset. Nav-R2 is trained via **ONLY SFT** receiving **ONLY RGB observations** from **ONLY first-person view**, and achieves the best SR on the val-unseen split. 
<p align="center">
 <img src="https://github.com/AMAP-EAI/Nav-R2/raw/main/figs/main-results.png" width="100%">
</p>

## Citation
If you find our work helpful or inspiring, please feel free to cite it.

```bibtex
@article{zhou2025navr2,
  title={Nav-R2: Dual-Relation Reasoning for Generalizable Open-Vocabulary Object-Goal Navigation},
  author={Authors names and affiliations will be added after review},
  journal={arXiv preprint arXiv:2512.02400},
  year={2025}
}
```