Zhengpu Wang
Papers
3
Total Citations
28
H-Index
2
About
Zhengpu Wang is a rising researcher in intelligent robotics and autonomous manipulation, with a focus on neurodynamics, kinematic calibration, and dexterous grasping. His work bridges theoretical control methods and practical robotic applications, particularly for space and industrial manipulators. Wang’s 2023 paper on neurodynamics-based configuration transformation, which has garnered 15 citations, introduces two intelligent approaches for robot manipulators, advancing real-time adaptive control. He also contributed a highly cited study on kinematic calibration using visual measurement systems and an Extended Kalman Filter (12 citations), addressing pose accuracy challenges in space manipulators by mitigating noise in vision-based sensing. Most recently, his 2025 work, DexMGNet, proposes a novel multi-mode dexterous grasping framework that leverages generative models to detect robust grasps in cluttered scenes—a critical step for humanoid robot manipulation. With growing citation impact and a trajectory toward solving complex, real-world robotic challenges, Wang’s research is shaping the future of autonomous, adaptive, and precise robotic systems.
Research Focus
Key Achievements
Top Papers
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