Kah Bin Lim
Papers
14
Total Citations
271
H-Index
8
About
Kah Bin Lim is a pioneering researcher in the intersection of robotics and minimally invasive surgery, with a primary focus on developing intelligent robotic systems for interventional medicine. His major contributions lie in the application of deep reinforcement learning to robot-assisted surgical training and needle insertion, notably demonstrated in his 2019 work on laparoscopic training (59 citations) and flexible needle insertion using universal distributional deep reinforcement learning (35 citations). Lim has also made significant advances in tumor treatment, including a robotic system for overlapping radiofrequency ablation in large tumors (55 citations) and robot-assisted RF ablation with interactive planning and mixed reality guidance. His work on projection-based visual guidance for needle insertion (32 citations) and augmented reality robotic surgery with intraoperative visual guidance (20 citations) has been instrumental in enhancing surgical precision. With a career spanning from early work on tactile sensors and robotic manipulators to cutting-edge AI-driven surgical systems, Lim's research has accumulated substantial impact, particularly in improving the accuracy, safety, and training efficiency of minimally invasive procedures.
Research Focus
Key Achievements
Top Papers
- 1Robot-Assisted Training in Laparoscopy Using Deep Reinforcement Learning59 citations · 2019
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- 4Projection-based visual guidance for robot-aided RF needle insertion32 citations · 2013
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- 6A pragmatic 3D visual servoing system17 citations · 2003
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- 9Low cost tactile gripper using silicone rubber sensor array8 citations · 1988
- 10Application of discrete learning control to a robotic manipulator5 citations · 1996