Young-Loul Kim
Papers
14
Total Citations
367
H-Index
9
About
Young-Loul Kim is a robotics researcher whose work spans robotic assembly, force control, and human-robot interaction safety — areas where precision engineering meets intelligent automation. With over 350 cumulative citations, Kim has established a strong reputation for solving fundamental challenges in industrial robotics. Kim's most influential contributions lie in the domain of robotic assembly, particularly peg-in-hole tasks. His geometry-based guidance algorithms for complex-shaped pegs (68 citations) and force/torque sensor-driven hole detection methods (65 citations) have provided practical frameworks that significantly reduce positional uncertainty in precision assembly operations. His 2019 work integrating reinforcement learning with movement primitives for contact tasks (50 citations) reflects a forward-looking pivot toward adaptive, learning-based robotic control. Equally notable is Kim's sustained focus on human-robot safety. His collision detection algorithm distinguishing intended contact from unexpected collisions (60 citations) addresses a critical challenge in collaborative robotics, complemented by follow-up work on redundant manipulators and model-free adaptation schemes. His research on variable stiffness actuators and direct teaching algorithms further demonstrates a commitment to making robots both safer and more intuitive to deploy in real-world manufacturing environments.
Research Focus
Key Achievements
Top Papers
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- 4Reinforcement learning based on movement primitives for contact tasks50 citations · 2019
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- 9Direct teaching algorithm based on task assistance for machine tending9 citations · 2016
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