Mario Rosenfelder
Papers
11
Total Citations
134
H-Index
6
About
Mario Rosenfelder is a robotics and control systems researcher whose work sits at the intersection of model predictive control (MPC), multi-robot systems, and data-driven methods. His research focuses primarily on cooperative mobile robotics, with particular emphasis on formation control and non-prehensile object transportation using non-holonomic platforms such as differential-drive vehicles. Rosenfelder's most significant contributions center on distributed nonlinear MPC frameworks that enable teams of robots to coordinate efficiently without centralized oversight. His 2022 paper on cooperative distributed nonlinear MPC for mobile robot formations has garnered 41 citations, establishing him as a notable voice in decentralized multi-robot control. Complementing this, his work on cooperative object transportation — spanning force-based control schemes, formation optimization, and both differential-drive and omnidirectional platforms — demonstrates a broad yet cohesive research agenda with over 25 citations across multiple studies. More recently, Rosenfelder has ventured into data-driven control, critically examining Koopman operator-based surrogate models for non-holonomic robots. His provocatively titled finding — "Data does not replace geometry" — argues that structural geometric knowledge remains indispensable even in machine learning-driven approaches, offering an important cautionary perspective for the field. With a growing citation record and experimentally validated results, Rosenfelder is emerging as a rigorous contributor to autonomous multi-robot systems research.
Research Focus
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Top Papers
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- 9On Koopman-based surrogate models for non-holonomic robots3 citations · 2024
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