用人类反应识别机器人连续出错，提升人机交互可靠性
Training Models to Detect Successive Robot Errors from Human Reactions
- 通过分析视频中人类行为特征，建模错误累积时的反应变化
- 准确率达93.5%检测错误，84.1%区分连续失败阶段
- 适合研究人机交互、自适应机器人系统的设计者
随着机器人融入社会，有效检测其错误对人机交互（HRI）至关重要。当机器人反复出错时，如何判断何时应调整行为？人类会通过言语和非言语信号逐渐表现出更强烈反应——从困惑、语调变化到明显沮丧与不耐烦。尽管已有研究证明人类反应可指示机器人故障，但很少探讨这些动态变化如何揭示连续性失败。本研究利用机器学习方法，从26名参与者与持续产生对话错误的机器人互动中提取视频数据的行为特征，为每位用户训练个性化模型。最佳模型在错误检测上达到93.5%准确率，在连续失败阶段分类上达到84.1%准确率。建模人类反应的演进过程，显著提升了对重复交互失败的检测与理解能力。
原文摘要 · Abstract (English)
As robots become more integrated into society, detecting robot errors is essential for effective human-robot interaction (HRI). When a robot fails repeatedly, how can it know when to change its behavior? Humans naturally respond to robot errors through verbal and nonverbal cues that intensify over successive failures-from confusion and subtle speech changes to visible frustration and impatience. While prior work shows that human reactions can indicate robot failures, few studies examine how these evolving responses reveal successive failures. This research uses machine learning to recognize stages of robot failure from human reactions. In a study with 26 participants interacting with a robot that made repeated conversational errors, behavioral features were extracted from video data to train models for individual users. The best model achieved 93.5% accuracy for detecting errors and 84.1% for classifying successive failures. Modeling the progression of human reactions enhances error detection and understanding of repeated interaction breakdowns in HRI.
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