Papers
5
Total Citations
37
H-Index
4
About
Shuo Zhu is a robotics researcher whose work bridges human-robot interaction, intelligent control, and advanced sensing. His key research areas include teleoperation systems, robot learning, fault-tolerant manipulation, and social robotics. Zhu's major contributions include developing a low-cost data glove using flex sensors for robot hand teleoperation, enabling intuitive control through finger flexion measurement—a practical innovation for accessible robotic interfaces. He also advanced robotic learning by integrating Beetle Antennae Search (BAS) with Extreme Learning Machines (ELM) to optimize KUKA iiwa robot performance, addressing critical challenges in weight and bias initialization. His work on sparsity-based methods for fault-tolerant manipulation of redundant robots addresses the heavy burdens of industrial tasks, enhancing reliability in manufacturing. Additionally, Zhu has explored multi-sensor emotional response systems for social robots and high-resolution non-line-of-sight imaging for robotic vision. With his most-cited papers accumulating over 37 citations, Zhu's research demonstrates both theoretical depth and practical impact, making him a notable contributor to the fields of robotics and intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2BAS Optimized ELM for KUKA iiwa Robot Learning11 citations · 2020
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