ABot-N0：关于VLA基础模型在多功能具身导航中的技术报告
ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation
AI总结：
ABot-N0通过统一的VLA基础模型实现多功能具身导航，结合认知大脑与动作专家架构，在多个基准测试中超越专用模型，支持动态环境中的长周期任务。
摘要：
具身导航长期以来因任务特定的架构而碎片化。我们引入了ABot-N0，一个统一的视觉-语言-动作（VLA）基础模型，实现了在五个核心任务上的“大统一”：点目标、对象目标、指令跟随、POI目标和人跟随。ABot-N0采用分层的“脑-动作”架构，将基于LLM的认知大脑用于语义推理，与基于流匹配的动作专家配对，以实现精确的连续轨迹生成。为了支持大规模学习，我们开发了ABot-N0数据引擎，整理了1690万专家轨迹和500万推理样本，覆盖7802个高保真3D场景（10.7平方公里）。ABot-N0在7个基准测试中实现了新的SOTA性能，显著优于专用模型。此外，我们的代理导航系统集成了规划器与分层拓扑记忆，使在动态真实环境中的稳健、长周期任务成为可能。
英文摘要：
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 $\text{km}^2$). 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
