Xiaojing Zhou

Guangdong University of Technology, Southeast University

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About

Xiaojing Zhou is a pioneering researcher at the intersection of robotics, computer vision, and human-robot interaction. Her primary research areas focus on robot learning from demonstration, telerobotic systems, and virtual environment modeling. Zhou's most impactful contribution is her object attribute guided framework for robot learning manipulations from human demonstration videos (2019, 3 citations), which enables robots to acquire new skills by observing natural human demonstrations without requiring special markers or unnatural behaviors—a significant step toward more intuitive robot programming. Her earlier foundational work on virtual environment construction in telerobotic systems (2006, 2 citations) established key methodologies for modeling geometrical, motional, and physical properties of robots and elastic objects, including deriving force-deformation equations essential for realistic teleoperation. Zhou's research bridges the gap between human demonstration and robotic execution, making robot learning more accessible and practical. Her work on virtual environment modeling remains relevant for modern teleoperation and simulation-based robot training. By enabling robots to learn from everyday human videos and creating realistic virtual environments for remote control, Zhou has contributed to making robotic systems more adaptable, user-friendly, and capable of learning complex manipulation tasks in real-world settings.