Papers
41
Total Citations
970
H-Index
17
About
Zhen Kan is a leading researcher in intelligent robotics and human-robot interaction, with a focus on robotic manipulation, exoskeleton control, and autonomous systems. His work bridges the gap between human collaboration models and robotic learning, enabling more natural and efficient human-robot cooperation. Kan’s notable contributions include the development of TF-Grasp, a transformer-based architecture for robotic grasp detection (148 citations), and asymmetric cooperation control for dual-arm exoskeletons (116 citations), which extends human collaborative skills to robotic systems. He has also pioneered saturated RISE feedback control for nonlinear systems (103 citations), providing robust solutions for uncertain environments. His research on skill transfer learning and reference trajectory reshaping for exoskeletons has advanced human-robot co-manipulation, while his work on task-driven reinforcement learning with action primitives addresses long-horizon manipulation challenges. With over 700 total citations across his most-cited papers, Kan’s impact is evident in both theoretical foundations and practical applications. His achievements include developing assimilation control methods for physical human-robot interaction and fast task allocation frameworks for heterogeneous robots, making him a key figure in the evolution of collaborative and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Saturated RISE Feedback Control for a Class of Second-Order Nonlinear Systems103 citations · 2013
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