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About

Xiaotie Deng is a pioneering researcher in algorithmic robotics and theoretical computer science, best known for his foundational work on robot exploration and mapping in unknown environments. His major contributions include developing competitive algorithms for autonomous navigation, particularly the problem of how a robot can learn an unknown environment with obstacles while minimizing worst-case traversal distance—a challenge he addressed in his highly cited 1998 paper "How to learn an unknown environment. I" (161 citations). Deng also advanced the theory of robot mapping with homogeneous markers, where robots navigate graph-based worlds without distinguishing nodes or edges, as detailed in his 1996 work (57 citations). His research on landmark selection strategies for path execution further refined self-localization techniques, aiming to reduce reliance on difficult-to-detect features. With a career spanning over two decades, Deng's work has profoundly influenced the fields of computational geometry, mobile robotics, and algorithmic learning, earning him recognition as a leading figure in theoretical computer science. His insights continue to guide students and researchers in developing efficient, provably optimal strategies for autonomous systems operating in uncertain environments.