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About

Mathias Thor is a robotics researcher whose work sits at the intersection of bio-inspired locomotion, neural control systems, and adaptive learning for legged and crawling robots. Drawing inspiration from biological organisms — including inchworms, dung beetles, and hexapods — Thor has made significant contributions to the design and control of robots capable of navigating complex environments with remarkable versatility. His most influential work, "iCrawl: An Inchworm-Inspired Crawling Robot" (2020, 78 citations), demonstrates his talent for translating evolved biological strategies into functional robotic systems. Equally impactful is his "Generic Neural Locomotion Control Framework for Legged Robots" (2020, 62 citations), which combines central pattern generators (CPGs) with radial basis function networks and black-box optimization — a framework that has become a reference point in neural locomotion research. Thor has consistently advanced the field of online learning for motor control, developing mechanisms for fast frequency adaptation and error-based learning that address real-world limitations of existing CPG approaches. His modular framework, MORF, further reflects a commitment to scalable, transferable robotics infrastructure. With over 290 cumulative citations and publications spanning foundational theory to applied systems, Thor represents an emerging leader in adaptive, biologically-grounded robot locomotion research.