VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model
CoRR(2024)
摘要
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
+3.7
introduced metrics that evaluate obstacle avoidance proficiency and perceptual
efficiency further corroborate the enhancements achieved by our method in ZSON
planning.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要