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Total Citations
2
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About
Dr. Xuejie Que is a leading researcher in the control and automation of flexible robotic systems, with a particular focus on advanced learning-based control strategies. Her work addresses the critical challenge of achieving precise tracking control in lightweight, flexible robots, which are prone to vibrations and modeling errors. Dr. Que’s most notable contribution is the development of a two-time scale primal-dual inverse reinforcement learning framework for flexible robots, published in 2024. This innovative approach tackles the problem of reference signal loss and enhances tracking accuracy by decoupling the system’s fast and slow dynamics, while simultaneously learning optimal control policies from expert demonstrations. Although her seminal paper has garnered 2 citations to date, its recency and the novelty of combining inverse reinforcement learning with two-time scale control signal a growing impact in the field. Dr. Que’s work is particularly relevant for applications requiring high-speed, precise motion in lightweight robotic arms, and her methodology offers a promising pathway for robust control in uncertain environments. Her research stands at the intersection of robotics, control theory, and machine learning, making her a rising scholar to watch in the domain of intelligent robotic systems.
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