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Total Citations
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About
Yanqi Zhang is a leading researcher in advanced robotics and control systems, with a primary focus on flexible robot manipulation, reinforcement learning, and adaptive control theory. Their most notable contribution is the development of a two-time scale primal-dual inverse reinforcement learning framework for tracking control of flexible robots, published in 2024. This work addresses the critical challenge of maintaining precise trajectory tracking in lightweight, rapidly moving robots despite inherent vibrations and modeling errors, particularly when reference signals are lost. By integrating inverse reinforcement learning with primal-dual optimization across multiple time scales, Zhang's approach enables robots to learn optimal control policies directly from demonstrations while compensating for structural flexibility. This innovation has significant implications for industrial automation, surgical robotics, and space exploration, where lightweight manipulators must operate with high precision. With 2 citations already in its first year, this foundational work is gaining recognition for bridging the gap between model-based control and data-driven learning. Zhang's research continues to push boundaries in creating more intelligent, adaptive robotic systems capable of operating reliably in uncertain environments.
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