Jin-Hyuk Lee
Papers
4
Total Citations
66
H-Index
3
About
Jin-Hyuk Lee is a pioneering researcher at the intersection of robotics, artificial intelligence, and human-machine interaction. His work centers on developing intelligent robotic systems that can perceive, learn from, and seamlessly collaborate with humans in real-world environments. Lee’s most significant contributions lie in wearable exoskeleton robots and dexterous manipulation. His landmark 2022 paper on real-time human activity recognition, which has garnered 52 citations, demonstrates how deep learning networks can interpret data from IMU and encoder sensors to enable exoskeletons to anticipate and assist with user motions—a critical advancement for rehabilitation and industrial support. Beyond wearables, Lee has advanced dexterous object manipulation by combining natural hand pose transformers with deep reinforcement learning, allowing anthropomorphic robot hands to perform complex, human-like tasks. He has also innovated in robotic inspection, developing a two-module pipe inspection system that integrates ultrasonic NDE devices for structural integrity evaluation. Most recently, Lee is addressing the critical challenge of reliable robotic communication, using deep learning to predict Quality of Service (QoS) in robotic networks. His interdisciplinary approach—spanning sensor fusion, deep learning, and mechanical design—positions him as a key figure in creating robots that are not only more autonomous but also more intuitive and responsive to human needs.
Research Focus
Key Achievements
Top Papers
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- 2Two-module robotic pipe inspection system with EMATs7 citations · 2014
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