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About

Guoxiang Zhao is a robotics and control systems researcher whose work centers on multi-robot motion planning, distributed algorithms, and safe reinforcement learning. His most significant contributions address the fundamental challenge of coordinating teams of robots to reach their goals efficiently while avoiding collisions — a problem critical to real-world autonomous systems deployment. Zhao is perhaps best known for his development of novel numerical algorithms to identify Pareto optimal solutions in multi-robot coordination, where no individual robot can improve its travel time without compromising another's performance. His 2020 paper on this topic has garnered 21 citations, with earlier conference work from 2018 accumulating an additional 10, reflecting sustained community interest in this approach. Building on this foundation, he has extended his research into scalable distributed frameworks, enabling near-optimal planning across larger robot teams without centralized computation. More recently, Zhao has pushed into the intersection of reinforcement learning and safety guarantees, developing distributed algorithms that ensure collision avoidance throughout the learning process. His 2024 work on risk-aware motion planning using 3D Gaussian Splatting demonstrates his engagement with cutting-edge perception technologies. Across his portfolio, Zhao consistently bridges theoretical rigor with practical robotic applications, making his research valuable to both control theorists and robotics engineers.