  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). In particular, our
approach presents a synergy of path and farsight descriptions to represent the
environmental context, enabling LLM to apply commonsense reasoning to ascertain
waypoints for navigation. Extensive evaluation on HM3D and HSSD validates
VoroNav surpasses existing benchmarks in both success rate and exploration
efficiency (absolute improvement: +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. Project page:
https://voro-nav.github.io
