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About

Hon-Sing Tong is a rising star in the field of medical robotics, with a focus on continuum robots, computer vision, and image-guided surgery. His research bridges the gap between soft robotic manipulation and real-time visual perception, addressing critical challenges in minimally invasive procedures. Tong’s most cited work, “Learning-Based Visual-Strain Fusion for Eye-in-Hand Continuum Robot Pose Estimation and Control” (2023, 29 citations), introduces a novel method that combines visual data with strain sensing to overcome the limitations of purely vision-based pose estimation—particularly in feature-deficient environments. This contribution enhances the accuracy and robustness of continuum robot control, a key enabler for safer, more precise interventions. In “Interactive Multi-Stage Robotic Positioner for Intra-Operative MRI-Guided Stereotactic Neurosurgery” (2023, 21 citations), he tackles the challenge of integrating robotic systems into the high-field MRI environment, developing a positioner that operates safely within the scanner while maintaining neurosurgical precision. His most recent work (2024) on shape-guided, configuration-aware learning for endoscopic image-based pose estimation further demonstrates his commitment to advancing flexible robotic instrument tracking. With a growing citation record and a focus on translational impact, Tong is establishing himself as a key contributor to next-generation surgical robotics.