Papers
10
Total Citations
68
H-Index
6
About
Hyuk Oh’s research lies at the intersection of computational neuroscience, motor learning, and human-robot interaction, with a central focus on how the brain controls reaching and dexterous manipulation. His work bridges neural modeling and robotic systems, aiming to replicate human-like motor performance in anthropomorphic machines. Oh’s most influential paper, “Motor Performance, Mental Workload and Self-Efficacy Dynamics during Learning of Reaching Movements” (2019, 16 citations), explores the cognitive-motor processes underlying skill acquisition across multiple practice sessions. He has also made significant contributions to neural architecture design, as seen in his 2015 paper (15 citations) on a unified framework for actual and mentally simulated bimanual movements. Oh’s cortically-inspired models for inverse kinematics computation in humanoid fingers (2012, 2016) address the complex control of mechanically coupled joints, advancing the field of robotic dexterity. His work has garnered over 70 citations, reflecting its impact on both theoretical understanding and practical applications in assistive robotics and neurorehabilitation. Oh’s recent 2025 study on brain biomarkers for robotic arm training signals his ongoing commitment to translating neural insights into real-world interfaces.
Research Focus
Key Achievements
Top Papers
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- 3An Object-Centric Paradigm for Robot Programming by Demonstration7 citations · 2015
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- 8Human-Robotic Collaborative Intelligent Control for Reaching Performance4 citations · 2013
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