Xiajing Li

Papers

Total Citations

101

H-Index

About

Xiajing Li is a researcher whose work sits at the intersection of robotics, control theory, and artificial intelligence, with a primary focus on the intelligent control of flexible robotic systems. Her most significant contribution lies in pioneering the application of reinforcement learning to suppress vibrations in single-link flexible manipulators—a critical challenge in lightweight, high-performance robotics. Her seminal 2017 paper, "Reinforcement learning control of a single‐link flexible robotic manipulator," which has garnered 91 citations, established a novel framework that combines the assumed mode method and Lagrange’s equation with adaptive learning algorithms. This approach enables robots to autonomously learn optimal control policies, effectively mitigating structural vibrations that compromise precision and stability. By integrating model-based dynamics with model-free reinforcement learning, Li’s work bridges classical mechanical modeling with modern AI-driven control, offering a robust solution for next-generation flexible manipulators used in manufacturing, aerospace, and surgical robotics. Her research not only advances the theoretical understanding of intelligent vibration control but also provides a practical pathway toward safer, more efficient, and adaptive robotic systems, marking her as a key innovator in the field of smart robotics.