Luke E. Miller

University of California San Diego, Radboud University Nijmegen

Papers

Total Citations

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About

Luke E. Miller investigates the cognitive and social foundations of human-robot interaction (HRI), focusing on how robot design and behavior shape human perception and acceptance. His early work, "Robot Form and Motion Influences Social Attention" (2015, 27 citations), demonstrated that a robot’s physical appearance and movement patterns critically affect how humans allocate social attention—a foundational insight for designing robots that feel natural and trustworthy. More recently, in "Interactive Human–Robot Skill Transfer: A Review of Learning Methods and User Experience" (2021, 9 citations), Miller synthesized advances in learning from demonstration, transfer learning, and user feedback, highlighting how interactive skill transfer can make robot programming more intuitive and accessible. His research bridges cognitive science and robotics, emphasizing that successful HRI depends not only on technical capability but on understanding human perceptual and social mechanisms. Miller’s work has informed the development of more adaptive, user-friendly robotic systems, and his reviews serve as key resources for researchers aiming to generalize robot operation across dynamic environments. By integrating human-centered design with machine learning, he continues to shape how robots learn from and collaborate with people.