Yong-Ho Na
Papers
1
Total Citations
4
H-Index
1
About
Yong-Ho Na is a robotics researcher whose work focuses on bridging the gap between simulation and reality in robotic manipulation, particularly through deep learning-based grasping. His most cited paper, "Learning to grasp objects based on ensemble learning combining simulation data and real data" (2017), addresses a critical challenge in robotics: the difficulty of collecting sufficient real-world training data for deep learning models. Na proposed an innovative ensemble learning approach that effectively combines simulated and real-world data to train grasping networks, reducing reliance on costly and time-consuming physical data collection. This work has garnered 4 citations, reflecting its niche but significant contribution to the field of robotic manipulation. Na’s research sits at the intersection of computer vision, reinforcement learning, and robotics, with a focus on data-efficient learning strategies. His contributions are particularly relevant for researchers working on transfer learning and domain adaptation in robotics, offering a practical solution to the data scarcity problem. While his citation count is modest, his work represents a thoughtful step toward making deep learning-based robotic grasping more accessible and scalable.
Research Focus
Key Achievements
Top Papers
- 1