Giorgos Mamakoukas
Papers
8
Total Citations
171
H-Index
5
About
Giorgos Mamakoukas is a robotics and control systems researcher whose work sits at the intersection of data-driven modeling, nonlinear dynamics, and autonomous systems. He is best known for pioneering the application of Koopman operator theory to robotics, developing systematic frameworks that transform complex nonlinear dynamics into tractable linear representations. His landmark 2019 paper on local Koopman operators for robotic control (77 citations) established a foundational methodology now widely adopted in the field, while subsequent work extended these ideas to soft robotic swimmers and real-time control applications. Mamakoukas has also made significant contributions to model predictive control, introducing automatic tuning methods that reduce the expertise burden in deploying data-driven MPC in robotic systems (36 citations). His research spans diverse platforms, from bio-inspired underwater vehicles to gliding robotic fish, where he has explored ergodic sampling strategies for environmental monitoring. His 2024 survey on Koopman operators in robot learning reflects his growing role in synthesizing and advancing this rapidly maturing subfield. Across his work, Mamakoukas consistently bridges rigorous mathematical theory with practical engineering, making sophisticated control-theoretic tools accessible and deployable on real robotic hardware.
Research Focus
Key Achievements
Top Papers
- 1Local Koopman Operators for Data-Driven Control of Robotic Systems77 citations · 2019
- 2Automatic Tuning for Data-driven Model Predictive Control36 citations · 2021
- 3Control-oriented Modeling of Soft Robotic Swimmer with Koopman Operators33 citations · 2020
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- 5Koopman Operators in Robot Learning5 citations · 2024
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