Add dataset card with metadata, benchmark overview, and sample usage

README.md

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+---
+license: apache-2.0
+task_categories:
+- robotics
+tags:
+- embodied-ai
+- visual-language-navigation
+- 3dgs
+---
+
+# ABotN-Bench
+
+[![Project Page](https://img.shields.io/badge/🌐-Project_Page-green)](https://amap-cvlab.github.io/ABot-Navigation/ABot-N1/)
+[![arXiv](https://img.shields.io/badge/arXiv-2607.10383-b31b1b.svg)](https://arxiv.org/abs/2607.10383)
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/amap-cvlab/ABot-Navigation)
+
+ABotN-Bench is a benchmark suite built on a high-fidelity 3D Gaussian Splatting (3DGS) reconstruction stack to advance the evaluation of closed-loop, social-rule-aware visual navigation in real-world indoor and outdoor environments. This benchmark was introduced in the paper [ABot-N1: Toward a General Visual Language Navigation Foundation Model](https://arxiv.org/abs/2607.10383).
+
+![Benchmark Overview](https://raw.githubusercontent.com/amap-cvlab/ABot-Navigation/main/assets/benchmark_overview.jpg)
+
+## Dataset Summary
+
+The benchmark consists of three complementary datasets:
+
+| Dataset | Task | Goal | Scenes | Episodes |
+|:----------|:-----|:-----|:-------|:---------|
+| **ABotN-PointBench** | Point-Goal | Navigate to (x, y) coordinates | 31 real-world 3DGS scenes (16 indoor + 15 outdoor) | 465 |
+| **ABotN-POIBench** | POI-Goal | Navigate to a named POI entrance | 11 commercial areas, 126k m² | 163 POIs |
+| **Short-Horizon OVON** | Object-Goal | Find and approach a target object category | 36 HM3D scenes (OVON Val-Unseen) | 2,443 |
+
+## Standard Agent Interface
+
+To evaluate your navigation model on these benchmarks, you can implement the standard agent interface from the `abotn-bench` package:
+
+```python
+from abotn_evaluator.interface.point_goal import BasePointGoalAgent, Observation, WaypointPrediction
+
+class YourAgent(BasePointGoalAgent):
+    def reset(self): 
+        pass
+        
+    def predict(self, observation: Observation) -> WaypointPrediction:
+        # observation.images: Dict[str, ndarray] — multi-view RGB (left/front/right)
+        # observation.target_position: ndarray — [front, left] in metres
+        # observation.distance_to_goal: float
+        return WaypointPrediction(waypoint=..., arrive=...)
+```
+
+## Citation
+
+If you use this dataset or benchmark in your research, please cite:
+
+```bibtex
+@misc{gong2026abotn1,
+  title={ABot-N1: Toward a General Visual Language Navigation Foundation Model},
+  author={Ruiyan Gong and Yingnan Guo and Junjun Hu and Jintao Kong and Xiaoxu Leng and Tianlun Li and Weize Li and Fei Liu and Zhicheng Liu and Jia Lu and Minghua Luo and Chenlin Ming and Yanfen Shen and Jiyue Tao and Zhengbo Wang and Mingyang Yin and Minqi Gu and Zihao Guan and Wei Guo and Guoqing Liu and Huachong Pang and Menglin Yang and Zeqian Ye and Xiaoxiao Geng and Zhining Gu and Honglin Han and Di Jing and Hongyu Pan and Mingchao Sun and Kuan Yang and Jianfang Zhang and Yanghong Chen and Ye He and Wei Mei and Jiahao Shi and Xiangpo Yang and Yanqing Zhu and Zedong Chu and Xiaolong Wu and Mu Xu},
+  year={2026},
+  eprint={2607.10383},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2607.10383},
+}
+```