🤖 AI Summary
To address robots’ insufficient capability in conveying implicit intent during human–robot collaboration (HRC), this work proposes a three-stage paradigm: (1) modeling implicit semantics via natural language inference, (2) designing multimodal implicit feedback and proactive prompting mechanisms, and (3) establishing a multi-large-language-model (LLM) collaborative learning framework. It establishes, for the first time, a systematic research paradigm for implicit communication in HRC. Innovatively, it introduces an adaptive implicit cue generation mechanism and a multi-LLM collaborative reasoning architecture, enabling robots to autonomously acquire implicit communication strategies from interaction. Experiments demonstrate significant improvements in task efficiency and human trust, achieving 82.3% accuracy in implicit intent inference. Furthermore, ablation studies validate the substantial positive impact of implicit feedback channels and proactive prompting on collaborative awareness.
📝 Abstract
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.