Paper Detail
Ruiyan Gong, Yingnan Guo, Junjun Hu, Jintao Kong, Xiaoxu Leng, Tianlun Li, Weize Li, Fei Liu, Zhicheng Liu, Jia Lu, Minghua Luo, Chenlin Ming, Yanfen Shen, Jiyue Tao, Zhengbo Wang, Mingyang Yin, Minqi Gu, Zihao Guan, Wei Guo, Guoqing Liu, Huachong Pang, Menglin Yang, Zeqian Ye, Xiaoxiao Geng, Zhining Gu, Honglin Han, Di Jing, Hongyu Pan, Mingchao Sun, Kuan Yang, Jianfang Zhang, Yanghong Chen, Ye He, Wei Mei, Jiahao Shi, Xiangpo Yang, Yanqing Zhu, Zedong Chu, Xiaolong Wu, Mu Xu
Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typically achieve this integration via monolithic policies that map observations directly to actions, yet they often suffer from coordinate drift and poor handling of long-tail semantics. Furthermore, these black-box mappings lack interpretability, hindering the simultaneous achievement of generality, robustness, and transparency. We present ABot-N1, a step toward a general Visual Language Navigation foundation model, that addresses these challenges by decoupling cognition from control via a slow-fast architecture guided by dual visual-language signals. More specifically, a slow vision-language reasoner performs explicit Chain-of-Thought reasoning while producing a pixel goal. This compact set of image-space anchor points serves as a universal interface for diverse tasks, including point-goal, object-goal, poi-goal, instruction-following, and person-following. Subsequently, a fast action expert leverages both the textual cues and the pixel guidance to generate continuous waypoints at the native control frequency. By bridging high-level intents and low-level control through pixel-grounded anchors paired with explicit linguistic traces, our approach ensures robust, generalizable, and interpretable navigation across simulation and real-world benchmarks. ABot-N1 establishes new state-of-the-art records, delivering massive gains specifically in urban-scale navigation: boosting POI arrival by 35.0% (to 77.3%) and achieving 95.4%/92.9% SR in complex indoor and outdoor scenes. It also maintains superior robustness across object-reaching, person-following, and instruction-following tasks. New Point-Goal/POI-Goal benchmarks are released as open source to advance the field of urban-scale navigation.
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@misc{gong2026abot,
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},
abstract = {Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typically achieve this integration via monolithic policies that map observations directly to actions, yet they often suffer from coordinate drift and poor handling of long-tail semantics. Furthermore, these black-box mappings lack interpretability, hindering the simultaneous achievement of generality, robustness, and transpa},
url = {https://huggingface.co/papers/2607.10383},
keywords = {huggingface daily},
eprint = {2607.10383},
archiveprefix = {arXiv},
}
{}