Zhiye Xiao

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About

Zhiye Xiao is a leading researcher in the fields of human–robot interaction, adaptive control, and robotic exoskeleton systems. His work focuses on bridging the gap between human neuromotor capabilities and robotic automation, particularly through skill transfer mechanisms that allow robots to emulate human limb impedance for stable and dexterous interaction with dynamic environments. His most cited paper, "Human–Robot Coordination Control of Robotic Exoskeletons by Skill Transfers" (2016), with 53 citations, introduces a novel control framework that combines human-inspired adaptation with robotic precision, advancing assistive technologies for rehabilitation and industrial applications. In his 2015 study, "Adaptive neural network control for uncertain MIMO robotic systems with time-varying delay and unknown backlash-like hysteresis" (6 citations), Xiao addresses critical challenges in nonlinear system control by integrating radial basis function neural networks (RBFNN) to compensate for delays and hysteresis. His contributions are instrumental in developing more robust, intelligent robotic systems capable of operating under real-world uncertainties. Xiao’s work not only enhances the performance of exoskeletons and multi-input-multi-output (MIMO) robots but also lays foundational knowledge for next-generation human–robot collaboration.