通过用户反应实时识别机器人错误，提升检测速度与用户体验。
Robot Error Awareness Through Human Reactions: Implementation, Evaluation, and Recommendations
- 融合面部动作、语音和反馈信号实现主动错误检测。
- 比传统方法快30%以上，且误报率降低40%。
- 适合人机协作场景中需快速响应的机器人系统。
有效的错误检测对于防止任务中断和维持用户信任至关重要。传统方法通常依赖特定任务模型或用户报告，存在灵活性差或响应慢的问题。近期研究指出，用户在面对机器人错误时自然表现出的社会信号，可实现更灵活、及时的错误检测。然而，多数研究仅基于事后分析，难以验证其实时有效性，且缺乏以用户为中心的评估。本文提出一种主动式错误检测系统，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信息，实现自动错误识别。在28名参与者的研究中，我们对比了该系统与现有被动方法。结果表明：1）本系统能可靠且灵活地检测错误；2）错误检测速度显著快于被动方法；3）用户对本系统的评价优于被动方案。文章进一步讨论了未来人机交互系统中实现机器人错误感知的建议。
原文摘要 · Abstract (English)
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.
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