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About

Andrew Bylard is a robotics researcher whose work spans safe autonomous systems, space robotics, and advanced motion planning for industrial manipulators. He has made notable contributions to the challenge of deploying robots in uncertain and constrained environments, most prominently through his development of a theoretically grounded safe active dynamics learning framework that enables autonomous robots to adapt efficiently while maintaining safety guarantees — a paper that has garnered 49 citations since 2022. Bylard is also a key contributor to the ReachBot project, an innovative small-bodied robot with long, extending arms designed for planetary exploration in low-gravity and rugged terrain, work that has attracted over 30 citations across multiple publications. His research further extends to real-time model predictive control for industrial manipulators, jerk-constrained trajectory planning, and GPU-accelerated collision detection, reflecting a broad command of both theoretical and applied robotics. Earlier work on gecko-inspired adhesive grippers and perception-constrained satellite servicing planning underscores his interest in versatile robotic grasping and space applications. Collectively, Bylard's research addresses some of the most pressing challenges in making robots safer, more capable, and more adaptable across terrestrial and extraterrestrial environments.