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About

Phoebe Liu is a leading researcher in human-robot interaction (HRI), specializing in data-driven methods for teaching robots socially appropriate behaviors. Her work focuses on enabling robots to learn natural social behaviors by observing and modeling human-human interactions, rather than relying on explicit programming. Liu’s most influential paper, "Data-Driven HRI: Learning Social Behaviors by Example From Human–Human Interaction" (99 citations), established a foundational approach for using sensor networks and crowd-sourced data to train robots. She further advanced this field by developing models for proactive social behavior (44 citations) and teaching service robots to reproduce human social conduct (24 citations). Notably, Liu addressed the subtle challenge of generating polite, context-aware gestures in "It's not polite to point" (22 citations), demonstrating that robots must adapt deictic behaviors when referring to people versus objects. Her more recent work explores curiosity-driven learning (17 citations), allowing robots to adapt beyond static training data. With over 220 total citations, Liu’s research bridges machine learning and social robotics, creating more intuitive and socially graceful autonomous systems that can navigate complex human environments.