ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation
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.
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Zedong Chu
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Shichao Xie
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Xiaolong Wu
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Yanfen Shen
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Minghua Luo
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Zhengbo Wang
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Fei Liu
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Xiaoxu Leng
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Junjun Hu
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Mingyang Yin
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Jia Lu
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Yingnan Guo
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Kai Yang
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Jiawei Han
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Xu Chen
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Yanqing Zhu
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Yuxiang Zhao
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Xin Liu
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Yirong Yang
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Ye He
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Jiahang Wang
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Yang Cai
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Tianlin Zhang
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Li Gao
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Liu Liu
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Mingchao Sun
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Fan Jiang
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Chiyu Wang
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Zhicheng Liu
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Hongyu Pan
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Honglin Han
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Zhining Gu
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Kuan Yang
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Jianfang Zhang
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Di Jing
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Zihao Guan
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Wei Guo
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Guoqing Liu
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Di Yang
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Xiangpo Yang
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Menglin Yang
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Hongguang Xing
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Weiguo Li
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Mu Xu
ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation
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.
A unified Vision-Language-Action model with a hierarchical architecture combining semantic reasoning and continuous trajectory generation achieves state-of-the-art performance across multiple embodied navigation tasks.
Vision-Language-Action LLM-based Cognitive Brain Flow Matching-based Action Expert hierarchical architecture embodied navigation expert trajectories 3D scenes Agentic Navigation System hierarchical topological memory long-horizon missions