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About

Andrew J. Taylor is a leading researcher in safety-critical control and robotics, whose work bridges rigorous theoretical guarantees with real-world implementation. His primary research areas include control barrier functions (CBFs), model predictive control (MPC), and Hamilton-Jacobi reachability, with a focus on ensuring safety and stability for complex autonomous systems under uncertainty. Taylor’s most impactful contribution is the comprehensive survey "Data-Driven Safety Filters" (2023, 97 citations), which unifies key safety frameworks for uncertain systems, providing a foundational resource for the field. He also pioneered the real-time unification of Nonlinear MPC with Control Lyapunov Functions (2020, 49 citations), enabling optimal performance with stability guarantees, and developed measurement-robust CBFs (2021, 34 citations) to handle imperfect state estimates—critical for real-world robotic safety. His work extends to dynamic locomotion, including online gait generation for bipedal robots (2022, 12 citations) and multi-layered safety for legged robots (2021, 9 citations). Beyond robotics, Taylor has contributed to medical imaging (X-ray-based distal locking, 2007, 9 citations) and crane dynamics (1998, 19 citations), demonstrating broad engineering impact. His research is essential reading for students and engineers working on safe autonomous systems.