Papers
10
Total Citations
130
H-Index
6
About
Ruofeng Wei is a leading researcher at the intersection of robotic surgery, computer vision, and autonomous navigation. His work focuses on enabling intelligent, data-efficient control of surgical robots—from laparoscopes to flexible endoscopes and continuum robots—by fusing visual perception with robot kinematics. Wei’s major contributions include developing stereo and monocular dense scene reconstruction methods that achieve metric-accurate depth estimation, a critical step for safe, autonomous surgical navigation. His 2022 paper on stereo dense reconstruction and localization for laparoscopic navigation has garnered 47 citations, reflecting its impact on the field. He has pioneered self-supervised and imitation learning approaches that operate under limited demonstrations, significantly reducing the data burden for training surgical robots. Notably, his 2024 work on data-efficient learning control of continuum robots in constrained environments introduces a stochastic control strategy with online model updates, enabling precise manipulation without analytical models. Wei’s research consistently addresses real-world surgical challenges—such as inter-patient anatomical variation and modeling uncertainty—pushing the boundaries of what is possible in robot-assisted minimally invasive surgery. His work is essential reading for anyone interested in the future of autonomous surgical systems.
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