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arXiv · 2603.06121

Glance-Say: Multimodal Human-Robot Collaboration and Intent Recognition via Sticky Glance

Yuzhi Lai·Shenghai Yuan·Peizheng Li·Benjamin Kiefer·Andreas Zell
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Abstract

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.

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Yuzhi Lai, Shenghai Yuan, Peizheng Li, Benjamin Kiefer, Andreas Zell. 2026-03-06. Glance-Say: Multimodal Human-Robot Collaboration and Intent Recognition via Sticky Glance. https://arxiv.org/abs/2603.06121

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