Su-Young Park
Papers
3
Total Citations
35
H-Index
2
About
Dr. Su-Young Park is a pioneering researcher at the intersection of robotics, digital twin technology, and deep reinforcement learning, with a focused mission to automate hazardous industrial tasks. His primary research areas include robotic automation for confined and dangerous workspaces, control system optimization for degraded machinery, and the integration of virtual simulation with real-world robotic operations. Dr. Park’s major contributions center on developing novel frameworks that combine digital twins—high-fidelity virtual replicas of physical systems—with advanced deep reinforcement learning algorithms, such as Proximal Policy Optimization (PPO), to enable robots to autonomously perform complex maintenance tasks. His most cited work, a 2024 study on robotic automation for nozzle dam replacement in nuclear power plants, has garnered 21 citations, demonstrating its immediate relevance to safety-critical industries. A subsequent 2024 paper on enhancing control performance for degraded robot manipulators, with 12 citations, further showcases his ability to address real-world equipment wear and tear. Dr. Park’s research not only advances theoretical knowledge but also offers practical, cost-effective solutions for replacing human workers in dirty, dangerous, and demanding environments, marking him as a key innovator in industrial robotics and automation.
Research Focus
Key Achievements
Top Papers
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