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About

Mark Lee is a prominent researcher in cognitive robotics and developmental robotics, whose work bridges psychology, neuroscience, and autonomous systems to advance how robots learn and adapt. His research focuses on biologically inspired learning frameworks that enable robots to acquire skills cumulatively and autonomously—mirroring the sensorimotor and cognitive development observed in human infants. Lee's most influential contribution, "A psychology based approach for longitudinal development in cognitive robotics" (2014, 60 citations), established a compelling paradigm for robots to learn novel behaviors without prior task knowledge or extensive pre-programming. Complementing this, his work on infant development timelines (2011, 42 citations) demonstrated how early childhood developmental stages could directly inform robotic learning architectures. His earlier research on error-driven active learning in growing radial basis function networks (2007, 30 citations) laid important algorithmic groundwork for adaptive robot learning. Through frameworks like PSchema—a Piagetian schema learning system for embodied agents—and studies on autonomous gaze control using the iCub robot, Lee has consistently pushed boundaries in developmental machine learning. With nearly 150 combined citations across key works, his contributions have meaningfully shaped the field of autonomous, experience-driven robotics.