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About

Xinru Xie is a leading researcher in robotics, specializing in force control, motion planning, and impedance learning for industrial and space applications. Her work bridges the gap between data-efficient machine learning and real-world robotic manipulation. Xie’s most influential contribution is the development of a reinforcement learning framework guided by prior policy knowledge for dual-arm free-floating space robots, which has garnered 49 citations. She also pioneered an efficient force control learning system based on variable impedance control, cited 45 times, that dramatically reduces the number of interactions needed for robots to master force-sensitive tasks—a breakthrough for industrial deployment. Her hybrid force/position control method using Kalman filters (15 citations) and adaptive impedance control for tasks like Chinese character writing (4 citations) further demonstrate her impact on precise, safe manipulation. Xie has also advanced calibration techniques for dual manipulator systems and explored bio-inspired spine motion in quadruped robots, showcasing her versatility. With over 130 total citations, her research is shaping the future of autonomous, compliant, and learning-enabled robots in both terrestrial and extraterrestrial environments.