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About

Matthias Hirschmanner is a leading researcher in human-robot interaction (HRI), with a focus on making robotic systems more intuitive, transparent, and socially adept. His work spans teleoperation, language learning, and assistive robotics, with a particular emphasis on how robots can better understand and respond to human behavior. Hirschmanner’s most cited paper (30 citations) introduces a virtual reality teleoperation system that allows users to control a humanoid robot by simply imitating their upper body movements—eliminating the need for cumbersome joysticks or keyboards. He has also made significant contributions to grounded language learning, developing systems that enable robots like Pepper to learn word-object and word-action mappings from human demonstration. His research on robotic gaze aversion (8 citations) systematically explores how subtle gaze behaviors influence human attitudes and interactions. Hirschmanner’s work on interaction style (17 citations) demonstrates that encouraging robot feedback can boost users’ self-efficacy. Notably, his recent co-design study with care home residents and workers (6 citations) highlights his commitment to inclusive, user-centered robotic technology. With a growing citation record and a portfolio that bridges technical innovation and social impact, Hirschmanner is shaping the future of accessible, human-aware robotics.