Richard Billingsley

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About

Richard Billingsley investigates the intersection of social robotics and human-robot interaction, with a focus on how robots can better understand and respond to human needs. His work centers on three key areas: the types of questions people naturally ask social robots in real-world settings, the modelling of human affective states to improve interaction quality, and the development of altruistic robot behaviour that goes beyond literal commands. In his most-cited study (2022, 4 citations), Billingsley conducted an in-the-wild experiment to categorise the questions people pose to a social robot acting as a receptionist, revealing context-dependent patterns essential for designing more intuitive question-answering systems. His 2017 paper on affective state modelling (3 citations) explores how robots can infer mood, emotion, personality, and even unspoken desires over time, while his work on the altruistic robot (2 citations) proposes a framework where robots act on inferred human intentions rather than explicit instructions. Though early in his career, Billingsley’s research lays critical groundwork for creating socially aware robots that anticipate human needs, with potential applications in service, healthcare, and assistive technologies.