PerspAct: 視点取得と能動的視覚によるLLMの状況的協調スキルの向上
PerspAct: Enhancing LLM Situated Collaboration Skills through Perspective Taking and Active Vision
LLMに他者の視点を明示的に与え、能動的な視覚探索と組み合わせることで、協調タスクにおける指示理解の精度が向上することを示した研究。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Sabrina Patania, Luca Annese, Anita Pellegrini, Silvia Serino, Anna Lambiase, Luca Pallonetto, Silvia Rossi, Simone Colombani, Tom Foulsham, Azzurra Ruggeri, Dimitri Ognibene
分類: cs.RO, cs.AI, cs.CL, cs.HC
原文アブストラクト
Recent advances in Large Language Models (LLMs) and multimodal foundation models have significantly broadened their application in robotics and collaborative systems. However, effective multi-agent interaction necessitates robust perspective-taking capabilities, enabling models to interpret both physical and epistemic viewpoints. Current training paradigms often neglect these interactive contexts, resulting in challenges when models must reason about the subjectivity of individual perspectives or navigate environments with multiple observers. This study evaluates whether explicitly incorporating diverse points of view using the ReAct framework, an approach that integrates reasoning and acting, can enhance an LLM's ability to understand and ground the demands of other agents. We extend the classic Director task by introducing active visual exploration across a suite of seven scenarios of increasing perspective-taking complexity. These scenarios are designed to challenge the agent's capacity to resolve referential ambiguity based on visual access and interaction, under varying state representations and prompting strategies, including ReAct-style reasoning. Our results demonstrate that explicit perspective cues, combined with active exploration strategies, significantly improve the model's interpretative accuracy and collaborative effectiveness. These findings highlight the potential of integrating active perception with perspective-taking mechanisms in advancing LLMs' application in robotics and multi-agent systems, setting a foundation for future research into adaptive and context-aware AI systems.
