Glance-Say: 視線と音声によるマルチモーダルな人機協調と意図認識
Glance-Say: Multimodal Human-Robot Collaboration and Intent Recognition via Sticky Glance
視線と音声を組み合わせた支援ロボット操作のためのインタラクションフレームワークを提案し、視線の揺れや意味の曖昧さに頑健な意図認識を実現した。
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著者: Yuzhi Lai, Shenghai Yuan, Peizheng Li, Benjamin Kiefer, Andreas Zell
分類: cs.RO
原文アブストラクト
Gaze and speech are promising interaction modalities for individuals with motor impairments, yet robust intent recognition in multi-object environments remains challenging due to micro-saccades, semantic ambiguity, and viewpoint changes. This paper presents a multimodal interaction framework for assistive robotic manipulation. We propose a sticky-glance algorithm that stabilizes gaze-based intent by jointly accumulating geometric distance and directional evidence, enabling robust real-time target selection and switching. We further introduce Glance-Say, a gaze-speech interaction paradigm in which gaze specifies objects and speech specifies actions, together with a continuous shared-control scheme that provides high-readiness robot motion and human-in-the-loop feedback. Experiments demonstrate a tracking rate of 0.92 for moving targets, selection accuracy of 0.97 for static targets, and reduced task duration. These results indicate improved robustness, efficiency, and usability over representative interaction paradigms.
