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About

Dashun Que is a robotics researcher specializing in surgical robotics, haptic perception, and autonomous manipulation. His work addresses critical challenges in robot-assisted minimally invasive interventions and dexterous grasping. Que’s most cited paper, “Tactile Grasp Stability Classification Based on Graph Convolutional Networks” (2021, 20 citations), introduces a novel deep learning approach using multi-sensory tactile data to predict whether a grasped object will slip—a fundamental problem for reliable robotic manipulation. In the domain of cardiovascular intervention, he has advanced teleoperated robotic systems for intravascular procedures. His paper “A Gaussian-Based Guidewire Segmentation and Tracking Method for Teleoperated Robotic Intravascular Interventions” (2021, 5 citations) proposes a robust method for real-time guidewire tracking under X-ray fluoroscopy, directly addressing radiation exposure risks to surgeons. Further extending this work, his research on “Master-slave Isomorphism Design and Guide Wire Segmentation of Robot for Vascular Intervention” (2021, 3 citations) develops a coordinated control system that enhances precision and haptic feedback in remote surgery. Que’s contributions lie at the intersection of tactile sensing, deep learning, and medical robotics, with potential to improve safety and autonomy in both industrial and surgical applications.