人間中心マルチモーダル観測からの能動的ロボット行動推論
Cognitive Action Reasoning for Proactive Robots from Human-Centered Multimodal Observations
明示的な指示なしに、人間と環境のマルチモーダルな手がかりからロボットが取るべき行動を判断する「能動的ロボット行動推論」を定式化し、実世界データセットProActionと参照モデルMMC2Actを提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Zhihao Gu, Kechao Zhu, Yuanfeng Wu, Mohan Liu, Ankit Kumar Shaw, ChenDong Hong, Xuanyu Chen, Dengchen Mei, Xu Tianyi, Lin Wang
分類: cs.RO, cs.CV
原文アブストラクト
Robots operating in human-centered environments are typically designed to execute explicit instructions, and most robot-learning datasets likewise pair observations with task instructions or low-level actions. Although recent work has begun to explore proactive embodied assistance, existing resources target different settings and action levels, leaving real-world human-centered multimodal decision-making underexplored. We formulate this problem as \textit{Proactive Robot Action Reasoning} (\textit{ProRobo}), an upstream cognitive decision problem in which a robot must determine which action to take based on multimodal human and environmental cues without explicit action instructions. To support ProRobo, we introduce \textit{ProAction}, a real-world multimodal dataset containing 10K samples of visual observations, audio signals, and text inputs across 12 daily-life scenarios in five common scenes. To construct cognitively grounded high-level action supervision, we develop a two-stage human-in-the-loop pipeline that combines appraisal-guided candidate generation with Affective Theory-of-Mind-guided human refinement, explicitly incorporating contextual judgment about human states, urgency, feasibility, and potential risk into action annotation. Based on this supervision, we benchmark representative Multimodal Large Language Models (MLLMs) and introduce \textit{MMC2Act}, a reference model that implicitly learns the mapping from multimodal observations to cognitively grounded high-level actions. Experiments across modality settings, subject-disjoint generalization, cross-dataset transfer, and human evaluation show that general-purpose MLLMs struggle with proactively reasoning high-level actions from multimodal cues, whereas training on \textit{ProAction} substantially improves performance.
