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About

Yuting Li is a versatile robotics and manufacturing researcher whose work spans robotic machining dynamics, human-robot collaboration in surgical environments, and intelligent sensing systems. Li's most influential contributions lie in the domain of robotic milling, where their research addresses one of the field's central challenges: the posture-dependent variability in tool-tip dynamics caused by the inherently low structural stiffness of industrial robots. Their 2020 paper on rapid prediction of posture-dependent frequency response functions, garnering 55 citations, and their 2018 work on inverse distance weighted stability prediction (39 citations) together provide practical, computationally efficient frameworks that significantly advance machining accuracy and surface quality in flexible manufacturing. Earlier in their career, Li made notable strides in surgical robotics, contributing to the development of the GestoNurse multimodal robotic scrub nurse system, which enables surgeons to request instruments via natural hand gestures and voice commands without disrupting procedural flow. These contributions, cited 35 and 28 times respectively, demonstrate meaningful progress in safe human-robot collaboration within high-stakes clinical settings. More recently, Li has explored intelligent storage through fused RFID and computer vision sensing. Collectively, their work reflects a rare breadth, uniting precision manufacturing, medical robotics, and smart logistics under a shared commitment to intelligent, adaptive robotic systems.