Dong-Hyeon Yeo
Papers
1
Total Citations
5
H-Index
1
About
Dong-Hyeon Yeo is a researcher in robotics and artificial intelligence, with a primary focus on reinforcement learning for autonomous navigation and dynamic obstacle avoidance. His most-cited work, "Robot Reinforcement Learning for Automatically Avoiding a Dynamic Obstacle in a Virtual Environment" (2015), demonstrates his early contributions to developing adaptive control strategies that enable robots to safely navigate unpredictable environments. This foundational study, which has garnered 5 citations, explores how reinforcement learning algorithms can be applied to real-time decision-making in virtual simulations, laying groundwork for more robust autonomous systems. Yeo's research addresses critical challenges in mobile robotics, particularly the integration of machine learning techniques with sensor-based perception to handle moving obstacles—a key requirement for applications in autonomous vehicles, warehouse logistics, and service robots. While his citation count reflects a focused, emerging body of work, his approach of combining simulation-based training with practical obstacle avoidance has relevance for researchers seeking to bridge the gap between virtual learning and real-world deployment. Yeo’s contributions highlight the potential of reinforcement learning to enhance robot autonomy in dynamic, human-centered environments.
Research Focus
Key Achievements
Top Papers
- 1