Xiao-Ming Wu

Ministry of Education of the People's Republic of China, Sun Yat-sen University

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About

Xiao-Ming Wu is a leading researcher in robotic manipulation, with a primary focus on dynamic grasping and 6-DoF grasp detection in unstructured environments. His work addresses the critical challenge of enabling robots to reliably grasp moving objects—a fundamental capability for real-world automation. Wu’s most-cited paper, “An Economic Framework for 6-DoF Grasp Detection” (2024), has already garnered 14 citations, reflecting its timely contribution to efficient, cost-effective grasp planning. In “MotionGrasp: Long-Term Grasp Motion Tracking for Dynamic Grasping” (2024), he pioneers a novel approach that moves beyond conventional neighbor-frame matching, instead leveraging long-term historical motion data to improve grasp stability on moving targets. This work directly tackles the limitations of prior methods that only consider the latest two frames, offering a more robust solution for dynamic environments. Wu’s research is highly impactful for the robotics community, providing both theoretical frameworks and practical algorithms that advance the state of the art in autonomous manipulation. His contributions are essential reading for students and researchers working on robotic grasping, motion planning, and real-time perception.