Papers
2
Total Citations
22
H-Index
2
About
Zheng He is a rising researcher at the forefront of human-machine interfaces and intelligent robotic control. His work centers on decoding complex human motor intent from surface electromyographic (sEMG) signals and advancing adaptive control strategies for robotic systems. He has made significant contributions to continuous finger kinematics estimation, notably developing a rotary transformer cross-subject model that overcomes the limitations of subject-specific models. This work, published in 2024 and already garnering 12 citations, introduces a transfer learning approach that enables rapid adaptation to new users—a critical step toward practical, plug-and-play prosthetics and exoskeletons. In parallel, He has advanced optimal control theory for robotics, proposing a single critic neural network-based reinforcement learning method for online optimal tracking control (2021, 10 citations). This innovation simplifies the traditional actor-critic architecture, enabling more efficient real-time control of robotic manipulators. With his work bridging biosignal processing and adaptive control, Zheng He is establishing himself as a key contributor to next-generation, intuitive human-robot collaboration systems.
Research Focus
Key Achievements
Top Papers
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- 2