仮想現実における人間-ロボット相互作用のためのマルチモーダル社会的認識フレームワーク
Multimodal Socially Aware Framework for Human-Robot Interaction in Virtual Reality
音声・視覚・触覚を統合してロボットの発話と動作を生成するフレームワークをVR上で18人により評価し、自律システムは人間遠隔操作に及ばないものの自然さと社会適切性で中間点を上回った。
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: Richard Xu, Dakarai Crowder, Alexis E. Block, Wenzhen Yuan
分類: cs.RO
原文アブストラクト
Social robots must coordinate speech and movement while interpreting multimodal human input. We present a multimodal framework that integrates speech, vision, and touch to generate contextually appropriate robot speech and movement. We evaluated the framework in virtual reality with 18 participants across five social interaction scenarios, comparing its performance against a human-teleoperated baseline. Although participants rated the autonomous system above the neutral midpoint for both naturalness and social appropriateness, the teleoperated baseline received significantly higher ratings on both measures. Behavioral coding and qualitative feedback revealed that conversational timing, clarification of ambiguous requests, and spatial coordination were important factors contributing to this performance gap. These findings highlight key challenges in developing autonomous robots capable of natural, multimodal social interaction.
