让机器人无需地图就能在多层楼中找物导航，靠的是分步推理和楼层感知。
Stairway to Success: An Online Floor-Aware Zero-Shot Object-Goal Navigation Framework via LLM-Driven Coarse-to-Fine Exploration
- 用分层抽象建模楼梯与楼层关系，动态构建导航结构
- 通过粗到细的探索策略，结合大模型分析上下文决策
- 可在真实四足机器人上运行，支持零样本新物品导航
可部署的服务与配送机器人在多层建筑中导航至目标物体时面临挑战，因现有系统依赖单层假设和离线全局一致地图。多层环境带来跨层过渡与垂直空间推理难题，尤其在未知建筑中。尽管对象目标导航基准如HM3D和MP3D已体现多层现实，但当前方法缺乏在线、楼层感知的导航能力。为此，我们提出 extbf{ extit{ASCENT}}，一种在线的零样本对象目标导航框架，使机器人无需预建地图或针对新物体类别重新训练即可运行。其引入：(1) 多层抽象模块，动态构建包含楼梯感知障碍映射与跨层拓扑建模的层次化表示；(2) 粗到细推理模块，结合前哨点排序与大语言模型驱动的上下文分析进行多层导航决策。我们在HM3D和MP3D基准上评估，优于当前最先进的零样本方法，并在四足机器人上实现真实世界部署。
原文摘要 · Abstract (English)
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.
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