AI 中文总结
该研究针对传统人机交互控制忽略人类行为随机性的问题，设计了感知人类变异性的最优控制器，实验表明其在保持任务性能的同时提升了交互体验，为共享控制设计提供了新方向。
AI 中文摘要
人机交互（HMI）需要考虑人类运动行为本质的控制策略。传统的共享控制和力反馈辅助方法通常忽略人类行为的随机性，可能会限制性能和人类交互体验。本研究设计了实验设置，评估了一种新型的感知人类变异性的最优控制器。参与者在三种条件下执行物理耦合的力反馈交互任务：一种是旨在传统地降低整体变异性的控制器模式，一种是旨在维持人类自然变异性模式的感知变异性控制器模式，以及作为基准的仅人类控制条件。我们分析了行为变异性、任务性能和人类交互体验。结果表明，考虑自然运动变异性在保持任务性能的同时，显著提高了可用性方面的感知交互质量。这些发现强调了将人类随机运动特征纳入共享控制设计的重要性，并证明了所提出的控制策略在HMI以人为中心的控制设计中的可行性和益处。
英文摘要
Human-machine interaction (HMI) requires control strategies that account for the nature of human motor behavior. Conventional shared-control and haptic-assistance methods typically ignore the stochastic nature of human behavior, potentially limiting both performance and human interaction experience. In this study, we designed an experimental setting and evaluated a novel human-variability-aware optimal controller. Participants performed a physically coupled haptic interaction task in three conditions: a controller mode that aims at conventionally reducing overall variability, a variability-aware controller mode designed to maintain human natural variability patterns, and a human-only control condition serving as a baseline. We analyzed behavioral variability, task performance, and human interaction experience. The results show that considering natural movement variability significantly increased perceived interaction quality in terms of usability while maintaining task performance. These findings highlight the importance of incorporating stochastic human movement characteristics into shared-control designs and demonstrate the feasibility and benefits of the proposed control strategy for human-centered control design of HMI.
Comments19 pages, 7 figures
