Xueqian Guo
Papers
1
Total Citations
4
H-Index
1
About
Xueqian Guo is a leading researcher in surgical robotics, with a primary focus on motion planning, control, and imitation learning for orthopedic applications. His most cited work introduces a novel framework combining Constrained Dynamic Movement Primitives (CDMP) with constrained optimization to enable active robots to learn and autonomously execute complex surgical maneuvers, such as pedicle screw placement. This approach significantly reduces the reliance on surgeon skill for critical tasks like pose alignment and drilling path execution, directly addressing a major bottleneck in current orthopedic robot systems. With 4 citations on this key paper, his contributions are gaining recognition for improving surgical precision and autonomy. Guo’s research bridges the gap between robot learning and constrained motion control, offering a pathway toward safer, more efficient, and less operator-dependent surgical interventions. His work is particularly impactful for advancing autonomous capabilities in high-stakes procedures, positioning him as an emerging innovator in the field of medical robotics.
Research Focus
Key Achievements
Top Papers
- 1