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About

Xinyan Yan is a robotics researcher whose work sits at the intersection of probabilistic inference, Gaussian processes, and autonomous systems. Best known for pioneering contributions to motion planning and continuous-time trajectory estimation, Yan has helped reframe classical robotics problems through the lens of probabilistic modeling. His most influential work, "Gaussian Process Motion Planning" (2016, 141 citations), introduced a novel framework that treats trajectory generation as probabilistic inference, enabling smoother, collision-free paths while naturally handling task constraints — a significant departure from traditional optimization approaches. Building on this foundation, his research on incremental sparse Gaussian process regression advanced simultaneous trajectory estimation and mapping (STEAM), allowing mobile robots to elegantly manage asynchronous, sparse sensor data in continuous time. Further work explored approximately optimal motion planning for stochastic systems and adaptive probabilistic trajectory optimization, bridging reinforcement learning and inference-based control for dynamic, uncertain environments. Collectively, Yan's contributions have helped establish Gaussian process-based methods as a powerful paradigm in robot learning and planning, accumulating over 220 citations and providing foundational tools that continue to influence both theoretical research and practical autonomous system design.