Deployable service and delivery robots struggle to navigate multi-floor buildings to reach object goals, as existing systems fail due to single-floor assumptions and requirements for offline, globally consistent maps. Multi-floor environments pose unique challenges including cross-floor transitions and vertical spatial reasoning, especially navigating unknown buildings. Object-Goal Navigation benchmarks like HM3D and MP3D also capture this multi-floor reality, yet current methods lack support for online, floor-aware navigation. To bridge this gap, we propose \textbf{\textit{ASCENT}}, an online framework for Zero-Shot Object-Goal Navigation that enables robots to operate without pre-built maps or retraining on new object categories. It introduces: (1) a \textbf{Multi-Floor Abstraction} module that dynamically constructs hierarchical representations with stair-aware obstacle mapping and cross-floor topology modeling, and (2) a \textbf{Coarse-to-Fine Reasoning} module that combines frontier ranking with LLM-driven contextual analysis for multi-floor navigation decisions. We evaluate on HM3D and MP3D benchmarks, outperforming state-of-the-art zero-shot approaches, and demonstrate real-world deployment on a quadruped robot.

翻译：可部署的服务与配送机器人在多楼层建筑中导航以抵达目标物体时面临困难，因为现有系统受限于单楼层假设以及对离线、全局一致地图的需求。多楼层环境带来了独特的挑战，包括跨楼层转换和垂直空间推理，尤其是在未知建筑中导航。HM3D和MP3D等目标物体导航基准测试也反映了这一多楼层现实，然而现有方法缺乏对在线、楼层感知导航的支持。为弥补这一差距，我们提出了\textbf{\textit{ASCENT}}，一种用于零样本目标物体导航的在线框架，使机器人能够在无需预建地图或针对新物体类别重新训练的情况下运行。该框架引入了：（1）一个\textbf{多楼层抽象}模块，通过楼梯感知障碍物映射和跨楼层拓扑建模动态构建分层表示；（2）一个\textbf{由粗到精推理}模块，结合前沿区域排序与LLM驱动的上下文分析，用于多楼层导航决策。我们在HM3D和MP3D基准测试上进行了评估，性能优于最先进的零样本方法，并在四足机器人上展示了实际部署。