Qingxin Shi
Papers
8
Total Citations
80
H-Index
4
About
Qingxin Shi is a robotics researcher whose work spans surgical automation, intelligent inspection systems, and constrained robotic control. Their key research areas include robot-assisted surgery, physical human-robot interaction, and learning-based control for manipulators. Shi's most impactful contribution is the development of a virtual-fixture based drilling control system for robot-assisted craniotomy, which enables surgeons to perform piecewise collaborative drilling tasks more efficiently through learning from demonstration (40 citations). They also proposed the Road Checkpoints Robot (RCRo) system, integrating enhanced YOLO object detection with a 6-DOF manipulator for autonomous security inspections (22 citations). Shi has advanced control theory with fixed-time recurrent neural network learning control for robotic manipulators under time-varying constraints, and developed visuomotor policy learning methods for surgical task automation. Their work addresses practical challenges in rehabilitation robotics, including a novel parallel mechanism for ankle rehabilitation, and sensor calibration methods for physical human-robot interaction. With publications spanning from 2016 to 2024, Shi's research demonstrates a consistent focus on bridging theoretical control methods with real-world robotic applications in surgery, security, and rehabilitation.
Research Focus
Key Achievements
Top Papers
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- 4Visuomotor Policy Learning for Task Automation of Surgical Robot4 citations · 2024
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