Yeongoh Ko
Papers
5
Total Citations
32
H-Index
4
About
Yeongoh Ko is a rising researcher in robotics, specializing in the control and actuation of advanced robotic systems for medical and industrial applications. Their work primarily focuses on cable-driven parallel robots, magnetic capsule robots, and continuum robots, with a strong emphasis on integrating deep reinforcement learning and model-based control to enhance precision and robustness. Ko’s most cited paper, "Compensated Motion and Position Estimation of a Cable-driven Parallel Robot Based on Deep Reinforcement Learning" (2023, 15 citations), introduces a novel approach to improving motion accuracy in complex robotic systems. They have also made significant contributions to wireless medical robotics, as seen in "Enhanced Motion Control of Magnetically Actuated Capsule Robot Using MEMA" (2025, 5 citations), which advances noninvasive diagnostic procedures. Their development of a real-time simulator for robotic electromagnetic actuation (2024, 5 citations) and a double-loop robust control strategy (2025, 5 citations) demonstrates a commitment to overcoming nonlinear model uncertainties. With a growing citation record and innovative work in dynamic modeling using Cosserat rod theory, Ko is establishing a reputation for bridging theoretical control methods with practical robotic applications.
Research Focus
Key Achievements
Top Papers
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- 3Model-Based Real-Time Simulator for Robotic Electromagnetic Actuation5 citations · 2024
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