Papers
2
Total Citations
9
H-Index
2
About
Mingwei Xu is a rising researcher in intelligent robotics and automation, with a focus on enhancing robotic manipulation and surgical precision through advanced learning and optimization techniques. Their work bridges deep reinforcement learning and dexterous control, addressing critical challenges in assembly efficiency and medical robotics. In their 2021 study on visual grasping, Xu developed a deep reinforcement learning strategy to stabilize peg-in-hole assembly, mitigating contact force fluctuations caused by uncertain disturbances—a key contribution to improving industrial automation reliability. This work has garnered 5 citations, reflecting its relevance to manufacturing and robotics. Xu’s research on optimal multi-manipulator arm placement for robotic surgery, also from 2021, tackles the preoperative challenge of positioning surgical arms to maximize dexterity while minimizing collisions, adapting to variable patient anatomies. With 4 citations, this study underscores Xu’s impact on surgical robotics, offering data-driven solutions to reduce reliance on expert intuition. Together, these contributions highlight Xu’s commitment to advancing autonomous systems in both industrial and medical domains, laying groundwork for more adaptive, efficient, and safe robotic operations. Their work is particularly valuable for students and researchers exploring the intersection of reinforcement learning, manipulation, and surgical automation.
Research Focus
Key Achievements
Top Papers
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