Papers
10
Total Citations
226
H-Index
7
About
Fangbo Qin is a leading researcher at the intersection of robotic manipulation, computer vision, and surgical automation. His work focuses on two core challenges: enabling robots to perform high-precision assembly and manipulation tasks, and developing intelligent vision systems for endoscopic surgery. In surgical vision, Qin pioneered multi-angle feature aggregation and contour supervision for real-time instrument segmentation, achieving 57 citations for his foundational 2020 work. He also introduced LC-GAN, a generative adversarial network that reduces the need for expensive labeled medical data by translating synthetic endoscopic images to realistic ones. On the robotics side, Qin’s skill learning framework for precision assembly—combining microscopic vision and force feedback—has garnered 49 citations, demonstrating efficient skill transfer from human demonstrations to robots. He further advanced deep reinforcement learning for insertion tasks, accelerating policy learning with expert demonstrations. His work on automated microelectrode hooking for brain-machine interfaces, published in 2023, highlights his commitment to translating vision-guided robotics into biomedical applications. With over 200 total citations, Qin’s research is shaping the future of autonomous surgical systems and intelligent manufacturing.
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