Seongwon Yoon

Pohang University of Science and Technology, Pohang Iron and Steel (South Korea)

Papers

Total Citations

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About

Seongwon Yoon is a researcher at the intersection of robotics, control systems, and applied machine learning. His work focuses on two key areas: developing robust manipulation strategies for service robots and advancing sim-to-real transfer learning for autonomous aerial vehicles. In his 2024 paper on suppressing violent sloshing flow in food serving robots—which has garnered 3 citations—Yoon introduced novel control methods to stabilize liquid containers during motion, directly addressing a critical safety and user-experience challenge in the growing field of domestic robotics. More recently, his 2025 work on a transformer-based dynamics model for quadrotor control demonstrates a pioneering approach to overcoming the data scarcity problem in reinforcement learning, achieving effective sim-to-real transfer with limited experimental data. This research, already cited once, holds promise for reducing the cost and risk of training autonomous drones. Yoon’s contributions are notable for their practical orientation, bridging theoretical advances in deep learning with real-world robotic applications. His work is particularly relevant for students and researchers interested in embodied AI, robot manipulation, and data-efficient learning for autonomous systems.