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About

Fabian Jenelten is a pioneering robotics researcher whose work sits at the intersection of legged locomotion, motion planning, and robot perception. His research has fundamentally advanced the ability of quadrupedal robots to navigate complex, unstructured environments through sophisticated optimization frameworks and real-time control systems. Jenelten's most celebrated contribution is his development of nonlinear model-predictive control for perceptive locomotion (237 citations), enabling robots to dynamically adapt foot placement and avoid collisions in challenging terrain. Building on this, his earlier work on online nonlinear motion optimization (204 citations) demonstrated that quadrupedal robots could reliably execute dynamic gaits — including running trots and pronking — in real time. His research on wheeled-legged quadrupeds further expanded the field by combining the agility of walking with the efficiency of driving (168 citations). Beyond locomotion, Jenelten has contributed to mobile manipulation through the ALMA framework (128 citations) and autonomous subterranean exploration as part of the award-winning CERBERUS team in the DARPA Subterranean Challenge (109 citations). His terrain-aware motion optimization work (TAMOLS) and GPU-accelerated elevation mapping reflect a consistent commitment to bridging perception and control. With over 1,400 cumulative citations, Jenelten's work represents some of the most impactful research in modern legged robotics.