让机器人实时识别并恰当回应用户打断，提升对话自然度。
Interruption Handling for Conversational Robots
- 基于人类对话模式设计实时中断识别与处理机制。
- 在111次打断中成功处理104次，准确率达93.69%。
- 适合研究人机交互、社交机器人行为设计的团队。
打断是人类交流中的基本要素，能提升对话的动态性与效率，但需各方有效管理。尽管机器人系统有所进步，当前最先进的系统仍难以实时处理用户主动打断。以往研究多聚焦于事后分析。为此，我们提出一个实时系统，可检测用户主动打断，并根据其意图（合作同意、合作协助、合作澄清或破坏性打断）进行响应。该系统基于人类-人类互动数据提炼的交互模式构建。我们将系统集成至大语言模型驱动的社交机器人中，通过21名参与者完成的限时决策任务和有争议讨论任务验证其有效性。结果表明，系统成功处理了104/111（93.69%）次用户主动打断。我们讨论了相关经验及其对对话机器人中断处理行为设计的启示。
原文摘要 · Abstract (English)
Interruptions, a fundamental component of human communication, can enhance the dynamism and effectiveness of conversations, but only when effectively managed by all parties involved. Despite advancements in robotic systems, state-of-the-art systems still have limited capabilities in handling user-initiated interruptions in real-time. Prior research has primarily focused on post hoc analysis of interruptions. To address this gap, we present a system that detects user-initiated interruptions and manages them in real-time based on the interrupter's intent (i.e., cooperative agreement, cooperative assistance, cooperative clarification, or disruptive interruption). The system was designed based on interaction patterns identified from human-human interaction data. We integrated our system into an LLM-powered social robot and validated its effectiveness through a timed decision-making task and a contentious discussion task with 21 participants. Our system successfully handled 93.69% (n=104/111) of user-initiated interruptions. We discuss our learnings and their implications for designing interruption-handling behaviors in conversational robots.
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