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About

Fei Liu is a robotics and medical engineering researcher whose work bridges deformable object manipulation, surgical robotics, and haptic systems. Liu's most influential contribution lies in developing physics-based and deep learning frameworks for controlling deformable materials — a notoriously complex challenge in robotics. Their 2023 paper on differentiable compliant position-based dynamics for rope-like object manipulation (42 citations) demonstrated a significant advance in autonomous robotic suturing, offering fast and accurate models of deformable rope physics. Complementing this, Liu's real-to-sim optimization framework for surgical tissue manipulation (2024) addresses the critical gap between simulated and real-world deformable object behavior in robotic surgery. Beyond manipulation, Liu has made meaningful contributions to medical imaging through an ODE-based recursive registration network (ORRN) for 4D lung CT deformable image registration, and to human-robot interaction through energy-based dual-user haptic training systems designed for supervised surgical skill transfer. Their 2019 survey on haptic applications in medicine further underscores a sustained commitment to translational medical technology. With growing citation counts across multiple disciplines, Liu's research is increasingly shaping the future of intelligent, autonomous robotic surgery.