Byungduk Kang
Papers
3
Total Citations
49
H-Index
3
About
Byungduk Kang is a leading researcher in the intersection of human motor control and robotic manipulation, with a primary focus on bio-inspired impedance control. His work investigates how the human arm achieves remarkable dexterity and compliance during contact tasks, and translates these biological strategies into advanced robotic systems. Kang’s most influential contribution is the development of a method to estimate multi-joint arm stiffness using electromyogram (EMG) signals and artificial neural networks. This seminal 2009 paper, with 36 citations, provides a non-invasive pathway to decode human motor intent, enabling robots to emulate human-like adaptability. He further advanced this field by modeling neural networks to predict dynamic multi-joint stiffness (2007, 9 citations) and by pioneering a natural actor-critic learning framework for robotic stiffness control (2008, 4 citations). Kang’s work bridges neuroscience and robotics, offering a principled approach for teaching robots superior motor skills in contact tasks, such as assembly or rehabilitation. His research is foundational for students and engineers seeking to understand how human-inspired compliance can make robots safer and more effective in unstructured environments.
Research Focus
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Top Papers
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