Xuefei Zhang

Papers

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About

Xuefei Zhang is an emerging researcher specializing in robotic force control and intelligent control systems, with a focus on developing adaptive algorithms for human-robot interaction and contact dynamics. Their most notable work, published in 2024, addresses a fundamental challenge in robotics: achieving stable and precise force regulation during robot-skin contact scenarios — a critical consideration for medical robotics, rehabilitation systems, and collaborative manufacturing environments. Zhang's primary contribution lies in innovatively integrating Gaussian Mixture Model and Gaussian Mixture Regression (GMM/GMR) algorithms with complementary compensation strategies to overcome the well-documented limitations of traditional impedance control methods. This approach enables robots to model and respond to contact relationships more intelligently, improving force stability in dynamic and sensitive interaction scenarios. The work has already accumulated 5 citations shortly after publication, signaling meaningful early interest from the robotics research community. Zhang's research sits at the intersection of machine learning and control theory — a rapidly growing field as robotics increasingly demands sophisticated, data-driven solutions for real-world deployment. For students and researchers exploring adaptive robot control, compliant manipulation, or human-robot collaboration, Zhang's contributions offer a promising methodological framework worth following as this body of work continues to develop.