Papers
16
Total Citations
267
H-Index
9
About
Wei Jing is a robotics researcher whose work spans industrial automation, robot planning, and intelligent manipulation. His most significant contributions lie at the intersection of coverage path planning, visual servoing, and robot calibration — areas critical to advancing autonomous industrial systems. Jing's most-cited work (53 citations) introduced a computational framework combining coverage planning and reinforcement learning for automated robotic inspection, addressing a highly repetitive yet costly challenge in manufacturing. Complementing this, his coverage motion planning methods for 3D shape inspection — formulated through Set Covering and Travelling Salesman Problems — demonstrate rigorous mathematical grounding in practical robotics (22–21 citations). His calibration research, leveraging Product-Of-Exponentials and Gaussian Processes to handle both geometric and loading-induced errors, has also garnered meaningful attention (28 citations). In learning-based robotics, Jing developed KOVIS (52 citations), an elegant keypoint-driven visual servoing system achieving zero-shot sim-to-real transfer — a notable feat in bridging simulation and real-world manipulation. His later hyper-network architecture further extended visual servoing to arbitrary desired poses, reflecting continued innovation in the field. With contributions spanning multi-robot navigation safety, 6D pose estimation, and autonomous maritime vessels, Jing has built a diverse yet coherent research portfolio making him a noteworthy figure in intelligent robotics and automation.
Research Focus
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Top Papers
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- 86D Pose Estimation with Correlation Fusion10 citations · 2021
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