Yeoneung Kim
Papers
1
Total Citations
18
H-Index
1
About
Yeoneung Kim is pioneering the integration of model predictive control with meta-reinforcement learning to create adaptive decision-making tools for mobile robots operating in dynamic environments. Their most-cited work, "Infusing Model Predictive Control Into Meta-Reinforcement Learning for Mobile Robots in Dynamic Environments" (2022, 18 citations), introduces a novel algorithm that enables robots to rapidly adapt to environmental changes by combining the predictive capabilities of MPC with the learning efficiency of meta-RL. This hybrid approach represents a significant contribution to autonomous navigation, addressing the critical challenge of real-time adaptation without requiring extensive retraining. Kim's research sits at the intersection of robotics, control theory, and machine learning, with potential applications ranging from warehouse automation to search-and-rescue operations. By bridging classical control methods with modern reinforcement learning frameworks, Kim is helping to develop more robust and flexible autonomous systems capable of handling the unpredictability of real-world environments.
Research Focus
Key Achievements
Top Papers
- 1