Yingke Wang

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About

Yingke Wang is a pioneering researcher at the intersection of human-robot interaction and nonverbal communication, with a primary focus on enhancing the expressiveness of humanoid robots. Her work addresses a critical gap in robotics: while large language models have revolutionized verbal communication, robots still struggle with the nuanced facial movements essential for natural human interaction. Wang’s most-cited paper, "Human-robot facial coexpression" (2024, 38 citations), explores how physical humanoid robots can better convey emotions through synchronized facial movements, moving beyond voice-only communication. Her research on "Learning realistic lip motions for humanoid face robots" (2026) tackles the challenge of lip-audio synchronization, a vital component of visual attention during conversation. Wang identifies two fundamental barriers to achieving lifelike lip behaviors, offering novel learning-based approaches to overcome them. Her work is foundational for creating more believable and engaging social robots, with direct implications for assistive technology, entertainment, and therapeutic applications. By bridging the gap between verbal fluency and physical expressiveness, Wang is shaping the next generation of humanoid robots capable of genuine, multimodal communication.