Xuedong Liu
Papers
3
Total Citations
19
H-Index
2
About
Xuedong Liu is a researcher at the forefront of intelligent manufacturing and human-robot collaboration, with a primary focus on robotic assembly, compliance control, and adaptive human-robot interaction. His work addresses critical challenges in smart manufacturing, particularly the programming-free assembly of complex components and the dynamic optimization of human-robot collaborative workshops. Liu’s most notable contributions include the development of deep reinforcement learning frameworks for variable stiffness compliant control, enabling robots to autonomously learn and execute peg-in-hole assembly tasks without manual programming. His 2024 paper on this topic has already garnered 13 citations, underscoring its timely impact on the field. Additionally, Liu has pioneered methods for online biotic fatigue detection in collaborative workshops, allowing for real-time task reallocation to enhance worker ergonomics and production flexibility. By optimizing non-diagonal stiffness matrices for compliance control, he has advanced the precision and adaptability of robotic systems in contact-rich environments. Liu’s work bridges the gap between theoretical reinforcement learning and practical industrial applications, offering scalable solutions for the next generation of smart factories. His research is essential reading for engineers and researchers aiming to integrate intelligent, human-aware robotics into manufacturing.
Research Focus
Key Achievements
Top Papers
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