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About

Matthew B. Luebbers is a human-robot interaction (HRI) researcher whose work sits at the intersection of cognitive science, robotics safety, and mixed reality technologies. His most influential contribution, "A Survey of Mental Modeling Techniques in Human–Robot Teaming" (2020), has garnered 89 citations and established him as a notable voice in understanding how humans and robots build shared mental models to collaborate effectively. This foundational work has helped shape how researchers approach trust, communication, and coordination in human-robot teams. Luebbers has also made meaningful contributions to the emerging field of augmented and mixed reality for HRI, participating in the development of the VAM-HRI workshop series, which convenes leading researchers to tackle real-world challenges in mixed reality human-robot environments. His investigations into human non-compliance with robot spatial ownership cues highlight critical safety implications for shared workspaces, while his more recent work on recency bias reveals how cognitive tendencies distort human perceptions of robot competence and trustworthiness. Rounding out his portfolio, his research on autonomous vehicle handover systems addresses pressing challenges in transportation safety. Across these threads, Luebbers consistently bridges fundamental cognitive science with applied robotics, making his work highly relevant for both theorists and practitioners in the HRI community.