VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model
This addresses the problem of enabling robots to navigate unfamiliar environments to find novel objects without prior training, representing an incremental advance with specific performance gains.
The paper tackles the Zero-Shot Object Navigation task for household robots by introducing VoroNav, a semantic exploration framework that uses a Reduced Voronoi Graph and large language models for planning, achieving absolute improvements of +2.8% Success and +3.7% SPL on HM3D and +2.6% Success and +3.8% SPL on HSSD.
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