Yong-Geon Kim
Papers
1
Total Citations
6
H-Index
1
About
Yong-Geon Kim is a robotics researcher whose work centers on reinforcement learning, force-based control, and sim-to-real transfer for industrial automation. His major contributions lie in developing data-driven methods to automate the tuning of impedance parameters, which are critical for safe and precise robotic assembly tasks involving contact forces. His most-cited paper, "Reinforcement Learning-based Sim-to-Real Impedance Parameter Tuning for Robotic Assembly" (2021, 6 citations), demonstrates a novel framework that bridges simulation and reality, enabling robots to learn optimal force control policies without exhaustive manual calibration. This work addresses a key bottleneck in deploying flexible assembly systems, reducing the need for expert human intervention. Kim’s research is notable for its practical focus on translating reinforcement learning algorithms into real-world manufacturing settings, a challenging domain where robustness and adaptability are paramount. His achievements include advancing the state of the art in sim-to-real transfer for contact-rich manipulation, a field with growing impact as industries seek more autonomous and resilient robotic solutions.
Research Focus
Key Achievements
Top Papers
- 1