用凝视+语音实现更精准的机器人协作，适合行动不便者使用
Glance-Say: Multimodal Human-Robot Collaboration and Intent Recognition via Sticky Glance
- 通过融合几何距离与方向信息稳定凝视信号，提升目标识别鲁棒性
- 静态目标选择准确率达97%，动态目标追踪率92%，任务耗时减少
- 支持连续人机协同控制，适合残障人士在多物体环境中高效操作
凝视与语音是行动障碍人群理想的交互方式，但在多物体环境中，因微跳动、语义模糊和视角变化，意图识别仍具挑战。本文提出一种辅助机器人操作的多模态交互框架。我们设计了粘性凝视算法，联合累积几何距离与方向证据，实现稳定实时的目标选择与切换。进一步提出Glance-Say交互范式，凝视指定对象、语音指定动作，并采用连续共享控制机制，提供高响应度机器人运动与人机反馈。实验表明，动态目标追踪率为0.92，静态目标选择准确率为0.97，任务时长显著缩短。结果表明，该方法在鲁棒性、效率与可用性方面优于现有范式。
原文摘要 · Abstract (English)
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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