Papers
7
Total Citations
84
H-Index
5
About
Wenrui Wang is a leading researcher at the intersection of robotics, control theory, and computer vision, with a particular focus on enabling robots to interact safely and intelligently with unknown or dynamic environments. Their work bridges the gap between autonomous navigation and physical contact, developing algorithms that allow robots to learn from demonstrations and adapt in real time. Wang’s most impactful contributions include pioneering impedance estimation techniques for robots operating in uncalibrated environments (16 citations) and advancing reinforcement learning frameworks integrated with dynamic movement primitives for obstacle avoidance (15 citations). A standout achievement is their development of a global adjustment method for photogrammetric processing of Chang’E-2 lunar stereo images (29 citations), which played a critical role in China’s lunar exploration program. Wang has also made significant strides in model predictive control for dynamic obstacle avoidance (10 citations) and homography-based visual servoing with neural-network-assisted filtering (6 citations). With a growing body of work spanning from space exploration to industrial manipulation, Wang’s research is shaping the future of adaptive, contact-rich robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2Impedance estimation for robot contact with uncalibrated environments16 citations · 2021
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