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About

Shi-Qi Liu is a pioneering researcher at the intersection of robotics, artificial intelligence, and interventional medicine, whose work is fundamentally reshaping robot-assisted cardiovascular surgery. His research focuses on three key areas: autonomous guidewire delivery, surgical skill assessment, and soft robotic systems for minimally invasive procedures. Liu's most impactful contribution is the development of a fast-moving piezoelectric micro-robotic fish with double caudal fins (87 citations), which demonstrates novel biomimetic locomotion principles. He has made significant strides in real-time guidewire analysis, creating a multi-functional framework for morphological and positional tracking in X-ray fluoroscopy (38 citations) that reduces radiation exposure and procedure time. Liu's innovative application of reinforcement learning to vascular robotics is particularly noteworthy—his CASOG framework (Conservative Actor-Critic with SmOoth Gradient, 16 citations) enables autonomous guidewire delivery, while his discrete soft actor-critic model (10 citations) addresses the nonlinear control challenges of soft-body instruments. His work on surgical skill assessment using dynamic warping manipulations (21 citations) provides objective evaluation methods for percutaneous coronary intervention, and his hydraulic soft robot for chronic total occlusions (8 citations) offers new hope for treating complete vascular blockages. Liu's research consistently bridges theoretical advances in machine learning with practical clinical solutions, earning over 220 total citations and establishing him as a leading voice in intelligent interventional robotics.