Linsen Zheng
Papers
1
Total Citations
29
H-Index
1
About
Linsen Zheng is a robotics researcher whose work centers on intelligent control systems, with a particular focus on visual servoing and neural network-based approaches for robotic manipulation. His most-cited paper, "A recurrent neural network approach for visual servoing of manipulators" (2017, 29 citations), addresses a fundamental challenge in robotics: enabling manipulators to precisely control their movements using visual feedback from an eye-in-hand camera. By formulating the visual servoing problem through recurrent neural networks, Zheng introduced a novel framework that enhances real-time kinematic control, allowing robots to adapt more effectively to dynamic environments. This contribution bridges the gap between traditional control theory and modern learning-based methods, offering a computationally efficient solution for tasks requiring high precision. While his citation count reflects the early-stage impact of his work, Zheng's research is notable for its practical relevance to industrial automation and autonomous systems. His approach demonstrates how neural architectures can be leveraged to solve classical control problems, making his work a valuable reference for researchers exploring the intersection of robotics, computer vision, and intelligent control.
Research Focus
Key Achievements
Top Papers
- 1A recurrent neural network approach for visual servoing of manipulators29 citations · 2017