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About

Gregory Pinte is a researcher whose work sits at the intersection of robotics, control theory, and energy optimization, with a particular focus on high-performance motion systems. His key research areas include time-optimal and energy-optimal control, model predictive control (MPC), and the application of these methods to robotic systems, most notably a badminton-playing robot. Pinte’s major contributions lie in developing and comparing model-free and model-based approaches for achieving time-optimal hit control, as demonstrated in his most-cited paper (26 citations), which provides a foundational comparison for practitioners. He further advanced the field by introducing Energy-Optimal Model Predictive Control (EOMPC), a method that balances motion speed with energy efficiency for point-to-point tasks. This work, detailed in his second most-cited paper (10 citations), shows how to minimize energy consumption while respecting motion time constraints. His research has practical impact, directly demonstrated through experimental validation on a badminton robot, where he solved the challenge of moving a racket from rest to a target state with non-zero velocity in minimal time. Pinte’s contributions are notable for bridging classical and modern control methods, offering clear, implementable solutions for energy-aware, time-constrained robotic motion.