Ki-Seo Kim
Papers
1
Total Citations
4
H-Index
1
About
Ki-Seo Kim is a researcher specializing in autonomous robotics and intelligent navigation systems, with a particular focus on the application of deep reinforcement learning to mobile robot control. His most recognized work centers on developing natural behavior learning frameworks for autonomous navigation, leveraging the power of deep Q-networks (DQN) — an innovative fusion of deep learning and Q-learning — to enable two-wheeled mobile robots to navigate dynamically through unknown environments using LiDAR sensor data. This research addresses one of the fundamental challenges in robotics: enabling machines to make real-time, adaptive decisions without prior environmental knowledge. Kim's contributions sit at the intersection of machine learning and robotics engineering, a rapidly growing field with significant implications for industrial automation, service robotics, and autonomous vehicles. His 2018 publication on natural behavior learning has garnered 4 citations, reflecting early-stage recognition within the research community and positioning his work as a foundational contribution to the growing body of literature on DQN-based robot navigation. For students and researchers exploring reinforcement learning applications in physical autonomous systems, Kim's work offers a practical and theoretically grounded entry point into this exciting domain.
Research Focus
Key Achievements
Top Papers
- 1