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About

Olalekan Ogunmolu is a robotics and control systems researcher whose work sits at a compelling intersection of advanced control theory, soft robotics, and medical applications. His research spans minimax game-theoretic control, neuro-adaptive systems, and vision-based robotic control, with a particular focus on developing robust solutions for real-world, safety-critical environments. Ogunmolu's most recognized contribution involves applying minimax iterative dynamic game frameworks to nonlinear robot control tasks, addressing fundamental challenges of model uncertainty and disturbance robustness in high-dimensional systems. Equally notable is his pioneering work in medical robotics: he led the development of a soft robotic patient positioning system for maskless head-and-neck cancer radiotherapy, a clinically significant innovation that replaces rigid immobilization masks with gentle, adaptive robotic control. His Soft-NeuroAdapt system introduced a three-degree-of-freedom neuro-adaptive correction platform capable of precisely managing patient pose during treatment, directly reducing risks to critical surrounding tissues. Drawing on RGB-D sensor fusion and visual servoing techniques, Ogunmolu demonstrated how intelligent perception can drive precise soft actuator control in sensitive medical settings. With cumulative citations across multiple venues, his body of work reflects a researcher dedicated to translating rigorous theoretical control methods into tangible, life-improving clinical technologies.