Cheolsoo Park

Imperial College London, Kwangwoon University

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Total Citations

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About

Cheolsoo Park is a leading researcher at the intersection of neuromorphic computing, human–machine interaction, and intelligent robotic control. His work is distinguished by pioneering the integration of spiking neural networks (SNNs) with reinforcement learning for real-world robotic applications. In a landmark 2025 study, Park achieved the first-ever application of an SNN-based Twin Delayed Deep Deterministic Policy Gradient algorithm for 3D robotic arm control, demonstrating how brain-inspired learning rules can dramatically improve precision and energy efficiency in autonomous systems. Earlier, his foundational 2011 paper on power-independent EMG-based gesture recognition—which has garnered 10 citations—introduced a novel method for detecting muscle contractions and translating them into four distinct hand gestures for robot control, a key contribution to wearable robotics. Park’s recent tutorial on brain-inspired learning rules for SNN-based control (2024) has quickly become a key reference, accumulating 5 citations. With a growing citation footprint and a clear trajectory from biosignal processing to neuromorphic control, Park is shaping the future of efficient, adaptive robotic systems.