In the realm of household robotics, the Zero-Shot Object Navigation (ZSON)
task empowers agents to adeptly traverse unfamiliar environments and locate
objects from novel categories without prior explicit training. This paper
introduces VoroNav, a novel semantic exploration framework that proposes the
Reduced Voronoi Graph to extract exploratory paths and planning nodes from a
semantic map constructed in real time. By harnessing topological and semantic
information, VoroNav designs text-based descriptions of paths and images that
are readily interpretable by a large language model (LLM). Our approach
presents a synergy of path and farsight descriptions to represent the
environmental context, enabling the LLM to apply commonsense reasoning to
ascertain the optimal waypoints for navigation. Extensive evaluation on the
HM3D and HSSD datasets validates that VoroNav surpasses existing ZSON
benchmarks in both success rates and exploration efficiency (+2.8% Success and
+3.7% SPL on HM3D, +2.6% Success and +3.8% SPL on HSSD). Additionally
introduced metrics that evaluate obstacle avoidance proficiency and perceptual
efficiency further corroborate the enhancements achieved by our method in ZSON
planning.

在家庭机器人领域，Zero-Shot Object Navigation 任务赋予了代理人在陌生环境中灵敏地穿越并定位来自新颖类别的物体而不需要事先明确训练。本文介绍了 VoroNav，一种新颖的语义探索框架，该框架提出了 Reduced Voronoi Graph 从实时构建的语义地图中提取探索路径和计划节点。通过利用拓扑和语义信息，VoroNav 设计基于文本的路径描述和图像，这些描述和图像可以被大型语言模型（LLM）轻松解释。我们的方法通过呈现路径和远视距描述的协同作用来表示环境背景，使 LLM 能够应用常识推理来确定导航的最佳航点。在 HM3D 和 HSSD 数据集上的广泛评估验证了 VoroNav 在成功率和探索效率（HM3D 上的成功率提高了 2.8%，SPL 提高了 3.7%，HSSD 上的成功率提高了 2.6%，SPL 提高了 3.8%）方面超过了现有的 Zero-Shot Object Navigation 基准。此外，引入了评估障碍物避免能力和感知效率的指标进一步证实了我们的方法在 Zero-Shot Object Navigation 规划中实现的改进。