Embodied navigation has long been fragmented by task-specific architectures. We introduce ABot-N0, a unified Vision-Language-Action (VLA) foundation model that achieves a ``Grand Unification'' across 5 core tasks: Point-Goal, Object-Goal, Instruction-Following, POI-Goal, and Person-Following. ABot-N0 utilizes a hierarchical ``Brain-Action'' architecture, pairing an LLM-based Cognitive Brain for semantic reasoning with a Flow Matching-based Action Expert for precise, continuous trajectory generation. To support large-scale learning, we developed the ABot-N0 Data Engine, curating 16.9M expert trajectories and 5.0M reasoning samples across 7,802 high-fidelity 3D scenes (10.7 km2). ABot-N0 achieves new SOTA performance across 7 benchmarks, significantly outperforming specialized models. Furthermore, our Agentic Navigation System integrates a planner with hierarchical topological memory, enabling robust, long-horizon missions in dynamic real-world environments.
@article{arxiv.2602.11598,
title = {ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation},
author = {Zedong Chu and Shichao Xie and Xiaolong Wu and Yanfen Shen and Minghua Luo and Zhengbo Wang and Fei Liu and Xiaoxu Leng and Junjun Hu and Mingyang Yin and Jia Lu and Yingnan Guo and Kai Yang and Jiawei Han and Xu Chen and Yanqing Zhu and Yuxiang Zhao and Xin Liu and Yirong Yang and Ye He and Jiahang Wang and Yang Cai and Tianlin Zhang and Li Gao and Liu Liu and Mingchao Sun and Fan Jiang and Chiyu Wang and Zhicheng Liu and Hongyu Pan and Honglin Han and Zhining Gu and Kuan Yang and Jianfang Zhang and Di Jing and Zihao Guan and Wei Guo and Guoqing Liu and Di Yang and Xiangpo Yang and Menglin Yang and Hongguang Xing and Weiguo Li and Mu Xu},
journal= {arXiv preprint arXiv:2602.11598},
year = {2026}
}