Xingqiang Jian

Papers

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About

Xingqiang Jian is a leading researcher at the intersection of robotics, artificial intelligence, and orthopedic surgery. His work focuses on advancing autonomous surgical systems, particularly through imitation learning and constrained optimization for motion planning and control. Jian’s most-cited paper, "Motion Planning and Control of Active Robot in Orthopedic Surgery by CDMP-Based Imitation Learning and Constrained Optimization" (2025), addresses a critical bottleneck in current orthopedic robots: their reliance on surgeons for complex tasks like pose alignment and drilling. By integrating Conditional Dynamic Movement Primitives (CDMPs) with optimization frameworks, he enables robots to learn and execute precise surgical maneuvers autonomously, reducing human dependency and enhancing procedural consistency. Though early in its citation trajectory, this work has already garnered 4 citations, signaling growing influence in the surgical robotics community. Jian’s contributions are pivotal for the next generation of active surgical robots, promising safer, more efficient pedicle screw placement and broader applications in minimally invasive orthopedics. His research bridges the gap between robotic dexterity and clinical safety, positioning him as a rising innovator in medical robotics.