通过人类反应实现机器人错误感知：实现、评估与建议
Robot Error Awareness Through Human Reactions: Implementation, Evaluation, and Recommendations
AI总结：
针对传统机器人错误检测方法灵活性不足、响应慢的问题，本研究结合用户行为信号、反馈与错误上下文构建主动检测系统，实验证明其检测更可靠快速、用户评价更优，为HRI系统的机器人错误感知提供建议。
摘要：
有效的错误检测对于防止任务中断、维持用户信任至关重要。传统方法通常依赖特定任务的模型或用户上报，存在灵活性不足或响应滞后的问题。近期研究表明，用户面对机器人错误时自然展现的社交信号，可实现更灵活、及时的错误检测，但多数研究依赖事后分析，其实时有效性尚不明确，且缺乏以用户为中心的评估。本研究开发了一套主动式错误检测系统，该系统结合用户行为信号（面部动作单元与语音）、用户反馈及错误上下文，实现自动错误检测。在一项28名受试者参与的研究中，我们将该主动系统与现有被动响应式方法进行对比。结果显示，该系统：1）能可靠且灵活地检测错误；2）检测速度快于响应式方法；3）用户评价优于响应式方法。本文还讨论了在未来人机交互（HRI）系统中实现机器人错误感知的相关建议。
英文摘要：
Effective error detection is crucial to prevent task disruption and maintain user trust. Traditional methods often rely on task-specific models or user reporting, which can be inflexible or slow. Recent research suggests social signals, naturally exhibited by users in response to robot errors, can enable more flexible, timely error detection. However, most studies rely on post hoc analysis, leaving their real-time effectiveness uncertain and lacking user-centric evaluation. In this work, we developed a proactive error detection system that combines user behavioral signals (facial action units and speech), user feedback, and error context for automatic error detection. In a study (N = 28), we compared our proactive system to a status quo reactive approach. Results show our system 1) reliably and flexibly detects error, 2) detects errors faster than the reactive approach, and 3) is perceived more favorably by users than the reactive one. We discuss recommendations for enabling robot error awareness in future HRI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
