LLMによる物体親和性転移で物体ゴールナビゲーションを進化させる
Advancing Object Goal Navigation Through LLM-enhanced Object Affinities Transfer
LLMから得た意味知識と学習ベースの物体親和性を動的に融合し、未知環境での物体ゴールナビゲーションの汎化性能を高めるフレームワークLOATを提案した。
著者: Mengying Lin, Shugao Liu, Dingxi Zhang, Yaran Chen, Zhaoran Wang, Haoran Li, Dongbin Zhao
分類: cs.RO
原文アブストラクト
Object-goal navigation requires mobile robots to efficiently locate targets with visual and spatial information, yet existing methods struggle with generalization in unseen environments. Heuristic approaches with naive metrics fail in complex layouts, while graph-based and learning-based methods suffer from environmental biases and limited generalization. Although Large Language Models (LLMs) as planners or agents offer a rich knowledge base, they are cost-inefficient and lack targeted historical experience. To address these challenges, we propose the LLM-enhanced Object Affinities Transfer (LOAT) framework, integrating LLM-derived semantics with learning-based approaches to leverage experiential object affinities for better generalization in unseen settings. LOAT employs a dual-module strategy: one module accesses LLMs' vast knowledge, and the other applies learned object semantic relationships, dynamically fusing these sources based on context. Evaluations in AI2-THOR and Habitat simulators show significant improvements in navigation success and efficiency, and real-world deployment demonstrates the zero-shot ability of LOAT to enhance object-goal navigation systems.
