基盤モデルによるフロンティア選択の信頼性探求：ゼロショット物体目標ナビゲーションに向けて
Exploring the Reliability of Foundation Model-Based Frontier Selection in Zero-Shot Object Goal Navigation
複数の専門家モデルによるフロンティア分析と合意形成で、基盤モデルを用いたゼロショット物体目標ナビゲーションの信頼性を高める手法を提案し、RoboTHORとHM3Dで最先端性能を示した。
著者: Shuaihang Yuan, Halil Utku Unlu, Hao Huang, Congcong Wen, Anthony Tzes, Yi Fang
分類: cs.RO
原文アブストラクト
In this paper, we present a novel method for reliable frontier selection in Zero-Shot Object Goal Navigation (ZS-OGN), enhancing robotic navigation systems with foundation models to improve commonsense reasoning in indoor environments. Our approach introduces a multi-expert decision framework to address the nonsensical or irrelevant reasoning often seen in foundation model-based systems. The method comprises two key components: Diversified Expert Frontier Analysis (DEFA) and Consensus Decision Making (CDM). DEFA utilizes three expert models: furniture arrangement, room type analysis, and visual scene reasoning, while CDM aggregates their outputs, prioritizing unanimous or majority consensus for more reliable decisions. Demonstrating state-of-the-art performance on the RoboTHOR and HM3D datasets, our method excels at navigating towards untrained objects or goals and outperforms various baselines, showcasing its adaptability to dynamic real-world conditions and superior generalization capabilities.
