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acvlab/ABotN-PointBench

ABotN-Bench 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. Dataset Summary The benchmark consists of three complementary datasets: Dataset Task Goal Scenes… See the full description on the dataset page: https://huggingface.co/datasets/acvlab/ABotN-PointBench.

sourceHugging Faceapache-2.0updated 1mo agoView on Hugging Face
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Dataset Card

ABotN-Bench

![Project Page](https://amap-cvlab.github.io/ABot-Navigation/ABot-N1/) ![arXiv](https://arxiv.org/abs/2607.10383) ![GitHub](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.

Benchmark Overview

Dataset Summary

The benchmark consists of three complementary datasets:

DatasetTaskGoalScenesEpisodes
ABotN-PointBenchPoint-GoalNavigate to (x, y) coordinates31 real-world 3DGS scenes (16 indoor + 15 outdoor)465
ABotN-POIBenchPOI-GoalNavigate to a named POI entrance11 commercial areas, 126k m²163 POIs
Short-Horizon OVONObject-GoalFind and approach a target object category36 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},
}