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About

Jack Close is a leading researcher in autonomous robotics, specializing in the intersection of control theory, machine learning, and safety-critical navigation. His work primarily focuses on developing robust algorithms for mobile robots, autonomous underwater vehicles (AUVs), and unmanned ground vehicles (UGVs) operating in dynamic, uncertain environments. Close’s major contributions include pioneering model-free safety-critical model predictive control (MPC) for mobile robots, which addresses parametric uncertainty and measurement inaccuracies—a paper that has garnered 8 citations since 2024. He has also advanced deep reinforcement learning (DRL) for mapless navigation, demonstrating how LiDAR configuration impacts goal-based navigation (6 citations). His innovative PID-fixed time sliding mode control for AUV trajectory tracking (5 citations) and distributed nonlinear MPC with relaxed control barrier functions for multi-agent AUV networks (2 citations) showcase his ability to bridge theory and experimental validation. Notably, his work on twin delayed deep deterministic policy gradient algorithms integrated with digital twin perception awareness (2 citations) highlights his commitment to safe, efficient UGV navigation. With over 20 citations across his key papers, Close is shaping the future of autonomous systems through rigorous, application-driven research.