Sticky-Glance: Robust Intent Recognition for Human Robot Collaboration via Single-Glance

• URL: http://arxiv.org/abs/2603.06121v1
• Date: Fri, 06 Mar 2026 10:27:12 GMT
• Title: Sticky-Glance: Robust Intent Recognition for Human Robot Collaboration via Single-Glance
• Abstract summary: We propose an object-centric gaze grounding framework that stabilizes intent through a sticky-glance algorithm.<n>The inferred intent remains anchored to the object even under short glances with minimal 3 gaze samples.<n> Experiments across dynamic tracking, multi-perspective alignment, a baseline comparison, user studies, and ablation studies demonstrate improved robustness, efficiency, and reduced workload.
• Score: 24.0711921276237
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Gaze is a valuable means of communication for impaired people with extremely limited motor capabilities. However, robust gaze-based intent recognition in multi-object environments is challenging due to gaze noise, micro-saccades, viewpoint changes, and dynamic objects. To address this, we propose an object-centric gaze grounding framework that stabilizes intent through a sticky-glance algorithm, jointly modeling geometric distance and direction trends. The inferred intent remains anchored to the object even under short glances with minimal 3 gaze samples, achieving a tracking rate of 0.94 for dynamic targets and selection accuracy of 0.98 for static targets. We further introduce a continuous shared control and multi-modal interaction paradigm, enabling high-readiness control and human-in-loop feedback, thereby reducing task duration for nearly 10 \%. Experiments across dynamic tracking, multi-perspective alignment, a baseline comparison, user studies, and ablation studies demonstrate improved robustness, efficiency, and reduced workload compared to representative baselines.

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