让机器人学会像人一样用隐含信息协作，提升团队默契。
Implicit Communication of Contextual Information in Human-Robot Collaboration
- 通过分析语言暗示对协作任务的影响，探索隐性沟通机制。
- 发现机器人主动反馈与预判行为能显著提升团队表现与感知。
- 设计多大模型系统，实现从人类隐性交流中学习的能力。
隐性沟通在人机协作（HRC）中至关重要，其中意图等上下文信息以语用含义形式传递，构成人际互动的自然部分。然而，使机器人在合作任务中恰当地运用隐性沟通仍具挑战。本研究分三阶段展开：首先探究语言暗示对协作任务的影响；其次考察机器人后向反馈与主动沟通的隐性线索如何影响团队绩效与感知，并探讨其应如何适应人类伙伴；最后设计并评估了一个多大语言模型（multi-LLM）机器人系统，该系统能够从人类的隐性沟通中学习。研究旨在增强机器人的自然沟通能力，推动其融入日常协作活动。
原文摘要 · Abstract (English)
Implicit communication is crucial in human-robot collaboration (HRC), where contextual information, such as intentions, is conveyed as implicatures, forming a natural part of human interaction. However, enabling robots to appropriately use implicit communication in cooperative tasks remains challenging. My research addresses this through three phases: first, exploring the impact of linguistic implicatures on collaborative tasks; second, examining how robots' implicit cues for backchanneling and proactive communication affect team performance and perception, and how they should adapt to human teammates; and finally, designing and evaluating a multi-LLM robotics system that learns from human implicit communication. This research aims to enhance the natural communication abilities of robots and facilitate their integration into daily collaborative activities.
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