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About

Lorenz Wellhausen is a prominent robotics researcher whose work sits at the intersection of legged locomotion, terrain perception, and autonomous navigation. His research has fundamentally advanced how quadrupedal and wheeled-legged robots perceive and traverse complex, unstructured environments — from industrial inspection sites to subterranean exploration challenges. Among his most influential contributions is his work on robust perceptive locomotion, which demonstrated how robots can use exteroceptive sensing to anticipate terrain before contact, enabling faster and more energy-efficient movement in the wild — a paper that has garnered over 729 citations. His self-supervised terrain property prediction framework (191 citations) further established a practical pipeline for robots to learn safe navigation paths without expensive manual labeling. Wellhausen also made notable contributions to GPU-accelerated elevation mapping, providing a computationally efficient environmental representation that has become widely adopted in mobile robotics. His applied research includes participation in the DARPA Subterranean Challenge and real-world ANYmal deployments in search-and-rescue and industrial inspection scenarios, underscoring the tangible real-world impact of his work. More recently, he has extended his expertise to wheeled-legged platforms navigating urban logistics environments. With a cumulative citation count exceeding 1,700, Wellhausen's contributions represent a cornerstone of modern perceptive robot locomotion research.