Xinpu Min
Papers
2
Total Citations
101
H-Index
2
About
Xinpu Min is a leading researcher in intelligent robotic systems, with a primary focus on automated polishing and force control for industrial applications. His work addresses critical challenges in manufacturing automation, particularly the precise control of contact forces during surface finishing operations. Min’s most influential contribution is the development of a method for industrial robot polishing of complex concave curved surfaces, which ensures stable, constant polishing pressure—a breakthrough published in 2018 that has earned 54 citations. Building on this, he pioneered an impedance control approach optimized through reinforcement learning (2022, 47 citations), enabling robots to autonomously maintain actuator contact force stability. This learning-based framework represents a significant advance in adaptive robotic manipulation. Min’s research has direct implications for high-precision manufacturing, reducing defects and improving efficiency in automated polishing systems. His work bridges control theory, machine learning, and practical robotics, making him a key figure in the evolution of intelligent manufacturing. With a growing citation impact, Min continues to shape how robots interact with complex surfaces, driving the next generation of autonomous industrial automation.
Research Focus
Key Achievements
Top Papers
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