Zhongli Wang
Harbin Institute of Technology, University of Jinan, Shandong University, Beijing Jiaotong University, Zhengzhou University of Science and Technology, Beijing Transportation Research Center, Beijing Institute of Technology, Chinese University of Hong Kong, China University of Petroleum, Beijing
Papers
20
Total Citations
264
H-Index
8
About
Zhongli Wang is a pioneering researcher in robotics, specializing in visual servoing, task planning, and semantic perception for service and industrial robots. His work bridges the gap between high-level reasoning and low-level control, with a focus on enabling robots to operate autonomously in dynamic, unstructured environments. Wang’s most influential contribution is his 2010 paper on dynamic eye-in-hand visual tracking using nonlinear observers (72 citations), which introduced a novel controller for locking moving objects in 3D space—a foundational advance for robotic manipulation and surveillance. He has since driven progress in semantic task planning, as seen in his 2020 work on home service robots (49 citations) and subsequent hybrid offline-online planning methods (31 citations), which integrate object-level semantic maps with probabilistic inference to enhance robot decision-making. His recent research on semantic grasping in stacking scenes (27 citations) and robust pouring skills through vision-audio fusion (6 citations) demonstrates a commitment to real-world applications. With over 200 total citations, Wang’s work has shaped modern robotics, earning recognition for its practical impact on service robots, industrial automation, and autonomous systems.
Research Focus
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- 10Robot gaining robust pouring skills through fusing vision and audio6 citations · 2022