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Walter Verdonck is a robotics researcher whose work has made significant contributions to the fields of industrial robot dynamics, system identification, and robot control. His research career has been defined by a sustained focus on developing rigorous, practical methods for accurately modeling and improving the performance of industrial robots. Verdonck's most influential contribution is his pioneering work on dynamic model identification for industrial robots, most notably his 2007 paper that has accumulated nearly 400 citations. This work established periodic excitation as a cornerstone technique, elegantly unifying experiment design, signal processing, and parameter estimation into a streamlined identification framework. His early research explored maximum likelihood estimation methods and the practical implementation challenges these approaches present, including the reliable derivation of joint velocities and accelerations from raw angle measurements. Beyond model identification, Verdonck made notable advances in industrial robot load identification and trajectory pre-compensation, the latter enabling improved path-tracking accuracy without modifying existing industrial controllers — a practically significant achievement. He also contributed to sensor-based robot task specification and robot programming by human demonstration, broadening his impact across multiple robotics subdisciplines. With a citation profile exceeding 600 cumulative references, his work remains a valuable resource for researchers and engineers developing high-performance robotic systems.