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About

Adrian Benigno Latupeirissa investigates the intersection of sound design, social robotics, and human-robot interaction (HRI), focusing on how non-verbal audio shapes user perceptions and social dynamics. His major contributions include advancing the “matching hypothesis” for robot voices, demonstrating that context critically influences the perceived appropriateness of a robot’s vocal characteristics (24 citations). He also pioneered interactive sonification for humanoid robots, developing PepperOSC—an interface enabling real-time mapping of robot movement to sound models—and probing aesthetic strategies for movement sonification, such as complexity and materiality, to enhance expressive robot gestures. His work extends to politeness behaviors in free-standing conversational groups, showing how vocal tone can persuade human decisions, and to semiotic analyses of cinematic robot sounds, revealing how film portrayals shape public expectations of social robots. With over 49 total citations across his publications, Latupeirissa’s research bridges engineering, psychology, and sound art, offering practical tools and theoretical insights for designing more socially attuned robots. His notable achievements include pioneering the systematic study of robot sound aesthetics and creating open-source interfaces that empower other researchers to explore sonic HRI.