Yung‐Kyun Noh
Papers
2
Total Citations
32
H-Index
2
About
Yung-Kyun Noh is a robotics researcher whose work bridges motion planning, multi-robot coordination, and machine learning for autonomous systems. His major contributions include developing a framework for cooperative aerial transportation using Parametric Dynamic Movement Primitives (PDMPs), which enables multiple aerial robots with manipulators to navigate cluttered environments safely. This work, published in 2017, has garnered 25 citations and addresses critical challenges in real-world drone logistics and search-and-rescue operations. Noh has also advanced autonomous navigation through domain adaptation techniques, employing adversarial learning to reduce reliance on expensive sensors and large labeled datasets. His 2017 paper on this topic, with 7 citations, demonstrates how synthetic data can bridge the gap between simulation and real-world deployment, making autonomous navigation more accessible and cost-effective. By tackling both the physical coordination of robots and the algorithmic challenges of perception under domain shift, Noh’s research contributes to the practical deployment of intelligent, collaborative robotic systems. His work is particularly relevant for students and researchers interested in the intersection of control theory, deep learning, and field robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Domain Adaptation Using Adversarial Learning for Autonomous Navigation7 citations · 2017