Xianfa Xue

Papers

Total Citations

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About

Xianfa Xue is a leading researcher in intelligent robotics, specializing in compliant control, robotic learning from demonstration, and human-robot interaction for smart manufacturing. His work addresses critical challenges in industrial automation, particularly for tasks requiring delicate force control and adaptive skill transfer. Xue’s most influential contribution is a compliant force control scheme for industrial robots, which enables safe and precise interactive operations like grinding 3C products—a paper that has garnered 12 citations for its practical impact on manufacturing. He further advanced the field with a robotic learning and generalization framework based on modified Dynamic Movement Primitives (DMP), achieving 8 citations by enabling robots to reproduce and generalize skills from continuous drag demonstrations. Notably, his 2022 work on a human-like learning framework for unknown environment interaction (3 citations) pioneers methods for robots to adapt to unstructured settings without explicit programming, bridging the gap between demonstration and real-world deployment. Xue’s research is instrumental in making industrial robots more flexible and autonomous, directly supporting the next generation of smart factories.