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About

Zhu Liang Yu is a leading researcher in brain-machine interfaces (BMI) and shared control systems for assistive robotics. His work focuses on seamlessly integrating human intent with robotic autonomy, particularly for wheelchair robots and high-degree-of-freedom manipulators. Yu’s most influential contribution is a Bayesian shared control framework that intelligently combines automatic robot control with brain-actuated commands, accounting for uncertainty in perception and action. This approach, detailed in his 2020 paper (67 citations), significantly enhances the performance and safety of brain-actuated wheelchairs. He further advanced the field by developing a self-adaptive shared controller that uses a brain state evaluation network to dynamically adjust control weights between the human and robot based on the operator’s cognitive state (31 citations). Yu has also pioneered methods for smooth robot movement using hierarchical dynamic movement primitives with deep reinforcement learning, and designed a steady-state visual evoked potential (SSVEP)-based BCI for controlling 4-DOF robotic manipulators. His work bridges the gap between human neural signals and complex robotic control, making assistive technologies more intuitive and reliable for users with motor impairments.