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About

Dennis Goldschmidt is a leading researcher in biologically inspired robotics, specializing in the development of adaptive locomotion and complex behaviors for legged robots. His work draws heavily from neurobiological studies of insects, such as stick insects and cockroaches, to engineer robust control systems for hexapod robots. Goldschmidt’s major contributions include pioneering methods for visual terrain classification to enable energy-efficient gait selection, and designing adaptive obstacle negotiation behaviors that mimic insect climbing strategies. His research has also advanced the integration of distributed recurrent neural forward models with synaptic adaptation and central pattern generators (CPGs) to produce sophisticated, adaptive walking patterns. With his most-cited paper, “Visual terrain classification for selecting energy efficient gaits of a hexapod robot,” accumulating 51 citations, and “Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots” reaching 49 citations, his work has significantly influenced the field of autonomous robotics. Goldschmidt’s notable achievements include demonstrating how reservoir computing and online adaptive forward models can achieve complex locomotion without extensive sensor suites, paving the way for more resilient and energy-efficient robots capable of navigating challenging, unstructured environments.