Yihui Li

Papers

Total Citations

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About

Yihui Li’s research lies at the intersection of human-robot interaction (HRI), imitation learning, and intelligent manufacturing, with a focus on enabling robots to understand and respond to human behavior in both social and industrial contexts. A key contribution is the development of a probabilistic fusion framework that integrates task-space and joint-space constraints for more adaptive and complex HRI skills—work that has been cited in foundational discussions on robot learning. Li has also advanced natural interaction by proposing a novel method for recognizing human interaction intention through emotion-driven behavioral cues, allowing social robots to respond in more personalized, “natural” ways. In the manufacturing domain, Li addressed practical challenges in electronic assembly by designing a dual-SCARA robot system for inserting odd-form components, tackling issues of quality, efficiency, and labor costs. Though early in their career, Li’s work has already garnered citations in both HRI and automation literature, reflecting its relevance to emerging research on socially aware robots and flexible production systems. These contributions position Li as a promising voice in the drive toward more intuitive and capable robotic partners.