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About

Baoru Huang is a dynamic researcher at the intersection of surgical robotics, computer vision, and language-guided manipulation, whose work is reshaping how intelligent robotic systems perceive, reason, and act in complex real-world environments. Her early contributions focused on improving the precision and intuitiveness of robotic surgery, most notably through a self-adaptive motion scaling framework for teleoperation (2018, 53 citations), which enables seamless master-slave surgical control. She has since advanced surgical perception through pioneering unified frameworks for simultaneous depth estimation and instrument segmentation in laparoscopic imaging (2022, 37 citations), alongside self-supervised depth estimation techniques grounded in 3D geometric consistency. Her research extends into flexible surgical robot modeling, where LSTM-based kinematic approaches tackle the notorious nonlinearities of tendon-driven systems. More recently, Huang has made significant strides in language-conditioned robotics, developing methods for language-driven grasp detection and affordance-pose estimation in 3D point clouds — work that is rapidly gaining traction with multiple 2024 papers already accumulating citations. Her open-source endovascular simulation platform, CathSim, further demonstrates her commitment to accessible, safety-conscious autonomous systems. With over 225 citations across a diverse and cohesive body of work, Huang represents a compelling voice in next-generation surgical and general-purpose robotics research.