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About

Thomas Gurriet is a control systems researcher whose work sits at the intersection of safety-critical control theory and real-world robotics. His primary contributions center on developing scalable frameworks that bridge the persistent gap between theoretical safety guarantees and their practical implementation in physical systems—a challenge that has long hampered the deployment of autonomous robots in human environments. Gurriet's most influential work, "A Scalable Safety Critical Control Framework for Nonlinear Systems" (2020, 63 citations), establishes a comprehensive approach combining control invariant sets with model-predictive control techniques, providing robust safety guarantees for complex nonlinear systems. His research on online active safety for robotic manipulators (33 citations) directly addresses human-robot collaboration in manufacturing, ensuring industrial robots can operate safely alongside human workers without collision. His complementary work on realizable set invariance conditions tackles the critical problem of making theoretical safety certificates achievable on real hardware under practical constraints. Beyond industrial robotics, Gurriet has applied his safety frameworks to medically significant platforms, including feedback control for a paraplegic exoskeleton (ATALANTE), enabling hands-free dynamic walking for patients. He has also explored unconventional domains such as microgravity-enabling aerial robotics. Together, his contributions represent a coherent effort to make rigorous safety guarantees computationally tractable and reliably deployable across diverse robotic systems.