Taeyoung Kim

Korea Advanced Institute of Science and Technology

Papers

Total Citations

H-Index

About

Taeyoung Kim is a robotics researcher whose work lies at the intersection of reinforcement learning, multi-agent systems, and autonomous navigation. His most-cited paper, "Path Planning of Cleaning Robot with Reinforcement Learning" (2022, 15 citations), tackles the practical challenge of energy-efficient household robotics, proposing intelligent path-planning strategies to reduce electricity consumption. Kim has also made notable contributions to multi-agent coordination, as seen in his work on a two-stage training algorithm for AI robot soccer (2021, 7 citations), which addresses cooperative learning among heterogeneous agents—a critical problem in decentralized robotics. In "Real-time 3-D Mapping with Estimating Acoustic Materials" (2020, 4 citations), he integrates visual perception with acoustic property estimation to enhance environmental mapping for mobile robots. More recently, his "Kick-motion Training with DQN in AI Soccer Environment" (2023) applies deep reinforcement learning to overcome the curse of dimensionality in robotic motion control. With a growing citation record and a focus on real-world deployable intelligence—from cleaning robots to soccer-playing agents—Kim’s research demonstrates how reinforcement learning can bridge simulation and practical autonomy.