Vuk Pajovic

Papers

Total Citations

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About

Vuk Pajovic is a leading researcher in robot locomotion and control, specializing in the intersection of optimization-based motion planning and real-time execution for legged and wheeled robots. His major contributions center on developing frameworks that bridge offline computation with online model predictive control (MPC), enabling robots to perform advanced mobility skills—such as dynamic walking, running, and agile maneuvers—that were previously computationally intractable in real time. His most-cited work, "Offline motion libraries and online MPC for advanced mobility skills" (2022, 76 citations), introduces a method for generating complex, long-horizon motions offline and deploying them online through efficient MPC, significantly expanding the capabilities of robots in unstructured environments. Pajovic’s research has been instrumental in advancing the practical deployment of highly dynamic robots, with his papers collectively accumulating hundreds of citations and influencing both academic robotics and industry applications. His work is recognized for its rigorous mathematical foundations and direct impact on autonomous systems, making him a key figure in the next generation of agile, adaptive robots.