Yong‐Gu Lee
Papers
2
Total Citations
53
H-Index
2
About
Yong-Gu Lee is a researcher advancing the intersection of robotics and artificial intelligence, with a primary focus on autonomous surgical systems. His key research areas include robot-assisted needle insertion, deep reinforcement learning, and medical robotics. Lee's major contributions lie in developing intelligent control frameworks for flexible needle steering, a minimally invasive technique that uses bevel-tipped needles to navigate soft tissue. His 2019 paper on universal distributional deep reinforcement learning for robot-assisted needle insertion has garnered 35 citations, demonstrating its influence in the field. In related work, he pioneered the use of Deep Q-Networks to simulate flexible needle insertion, addressing the critical challenge of reducing the prolonged training and experience required for surgeons to master this delicate procedure. By applying advanced reinforcement learning algorithms to medical robotics, Lee's research aims to enhance precision, safety, and accessibility in minimally invasive surgery. His work represents a significant step toward autonomous surgical assistants that can learn and adapt to complex, real-world clinical environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Simulation of Robot-Assisted Flexible Needle Insertion Using Deep Q-Network18 citations · 2019