Papers
22
Total Citations
467
H-Index
11
About
Zhehao Jin is an accomplished robotics and control systems researcher whose work spans visual servoing, reinforcement learning, impedance control, and human-robot collaboration. His research addresses some of the most challenging problems in intelligent robotics, bridging classical control theory with modern machine learning techniques to create adaptive, robust robotic systems. Jin's most influential contributions include a comprehensive survey on learning-based visual servoing control (68 citations) and pioneering work applying deep reinforcement learning to solve visibility-constrained visual servoing for mobile robots (68 citations). His research on Model Predictive Variable Impedance Control (62 citations) has advanced the field by enabling manipulators to dynamically balance precision and compliance in complex contact-rich tasks — a longstanding challenge in robotic manipulation. Beyond manipulation, Jin has made notable strides in demonstration learning, developing neural energy functions that guarantee stability without sacrificing accuracy (32 citations), and in multi-agent coordination through event-triggered predictive control frameworks. His hierarchical human-robot collaboration framework (36 citations) further demonstrates his breadth across autonomous and collaborative robotics. With over 370 total citations across a focused body of work produced largely between 2020 and 2023, Jin represents a rapidly emerging voice shaping the future of intelligent robotic control.
Research Focus
Key Achievements
Top Papers
- 1
- 2A survey Of learning-Based control of robotic visual servoing systems68 citations · 2021
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- 6An Optimal Variable Impedance Control With Consideration of the Stability29 citations · 2022
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- 8Gaussian process movement primitive20 citations · 2023
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