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About

Dongxu Zhou is a robotics researcher whose work sits at the intersection of robot motion planning, reinforcement learning, and robotics education. His research has made meaningful contributions to how both machines and students learn to navigate the complex challenges of robotic systems. Zhou's most cited work, a 2019 study on teaching robot kinematics using MATLAB and V-REP (24 citations), reflects his deep commitment to making robotics education more accessible and effective through simulation-based learning environments — a theme further developed in his earlier virtual laboratory design work. On the algorithmic frontier, Zhou has explored cutting-edge approaches to robotic arm motion planning, publishing two notable 2021 studies on residual reinforcement learning and curriculum reinforcement learning, collectively earning over 25 citations. These contributions address persistent challenges in training efficiency and convergence that limit real-world deployment of autonomous robotic systems. His development of Mirobot, a low-cost six-degree-of-freedom desktop educational robot, exemplifies his talent for bridging research and practical application, democratizing access to advanced robotics hardware. Across his portfolio, Zhou demonstrates a consistent vision: making intelligent robotics — both its practice and pedagogy — more efficient, accessible, and impactful for the next generation of engineers and researchers.