Papers
4
Total Citations
12
H-Index
2
About
Xiaoguang Han is an emerging researcher at the intersection of medical robotics, artificial intelligence, and surgical automation, with a focus on developing intelligent systems for robot-assisted spinal surgery and generalizable robotic manipulation. His most notable contribution lies in advancing force perception capabilities for robot-assisted laminectomy — a complex spinal procedure — through the application of imitation learning from human demonstrations. By comparing impedance model methods against imitation learning approaches, Han's 2024 work (8 citations) has helped establish a framework for transferring nuanced surgical skills to robotic systems, a critical step toward safer and more precise autonomous surgery. Complementing this, his research on milling force estimation and surgical state recognition further refines intraoperative intelligence, while his work on video generation for hand-object interaction (TASTE-Rob) expands the scope of robotic imitation learning beyond the operating room. Han has also contributed to broader strategic discussions on AI and robotics deployment in healthcare, particularly in the context of China's aging population and resource disparities. Though early in his career, his work bridges fundamental robotics research with pressing real-world clinical needs, positioning him as a promising voice in surgical and medical robotics.
Research Focus
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Top Papers
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