ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation
Authors: name: Zedong Chu, name: Shichao Xie, name: Xiaolong Wu, name: Yanfen Shen, name: Minghua Luo, name: Zhengbo Wang, name: Fei Liu, name: Xiaoxu Leng, name: Junjun Hu, name: Mingyang Yin, name: Jia Lu, name: Yingnan Guo, name: Kai Yang, name: Jiawei Han, name: Xu Chen, name: Yanqing Zhu, name: Yuxiang Zhao, name: Xin Liu, name: Yirong Yang, name: Ye He, name: Jiahang Wang, name: Yang Cai, name: Tianlin Zhang, name: Li Gao, name: Liu Liu, name: Mingchao Sun, name: Fan Jiang, name: Chiyu Wang, name: Zhicheng Liu, name: Hongyu Pan, name: Honglin Han, name: Zhining Gu, name: Kuan Yang, name: Jianfang Zhang, name: Di Jing, name: Zihao Guan, name: Wei Guo, name: Guoqing Liu, name: Di Yang, name: Xiangpo Yang, name: Menglin Yang, name: Hongguang Xing, name: Weiguo Li, name: Mu Xu

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 km^2). 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.