Papers
8
Total Citations
207
H-Index
5
About
Zhikai Yao is a leading researcher at the intersection of reinforcement learning (RL), robotic control, and assistive technology. His work focuses on developing data-driven, adaptive control systems for complex robotic platforms, including hydraulic manipulators and prosthetic limbs. Yao’s major contributions include pioneering the use of actor-critic reinforcement learning to achieve high-accuracy tracking control for 6-DOF hydraulic robotic manipulators—a notoriously difficult problem due to strong nonlinearities and unmodeled dynamics. He has also made significant strides in rehabilitation robotics, where his RL-based impedance control methods enable robotic knee prostheses to automatically adapt and coordinate with a user’s intact knee motion, mimicking natural gait patterns. With over 200 total citations, his most influential papers—such as “Data-Driven Control of Hydraulic Manipulators by Reinforcement Learning” (58 citations) and “Reinforcement Learning Impedance Control of a Robotic Prosthesis” (44 citations)—demonstrate the practical impact of his work. Notably, Yao has integrated disturbance observer techniques with learning-based control to enhance robustness in uncertain environments, and his recent explorations into deep Lagrangian networks promise to further refine dynamics modeling for next-generation robots. His research is shaping the future of autonomous, human-aware robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Data-Driven Control of Hydraulic Manipulators by Reinforcement Learning58 citations · 2023
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