Max Robotics — Global Service Robot Directory + US Certification

About

Matthew Egbert is a computational cognitive scientist whose research sits at the intersection of embodied cognition, robotics, and theoretical biology. His work centers on understanding **habit**, **agency**, and **sensorimotor behavior** as emergent, self-organizing phenomena rather than simple stimulus-response associations. His most influential contribution, "Modeling habits as self-sustaining patterns of sensorimotor behavior" (2014, 72 citations), challenged the dominant neuroscientific view of habits as mere stimulus-triggered responses, proposing instead a dynamical systems framework in which habits arise as self-perpetuating patterns of activity — a perspective with significant philosophical implications for cognitive science. Building on this foundation, Egbert developed a series of increasingly sophisticated computational models — including the Iterant Deformable Sensorimotor Medium (IDSM) and adaptive sensorimotor map (ASM) networks — to explore how minimal agents can autonomously generate goal-directed behavior. His 2019 work on heteroclinic network-based controllers further demonstrated how analytically tractable dynamical systems can illuminate the mechanics of robot behavior. Egbert's broader project draws on **enactive cognition**, pushing biologically inspired robotics beyond problem-solving toward understanding how problems themselves emerge through sensorimotor interaction — a genuinely novel and provocative reframing of artificial intelligence research.