Jaemin Yoon

Papers

Total Citations

H-Index

About

Jaemin Yoon is a researcher at the forefront of robotic manipulation, with a primary focus on dexterous grasping and perception for domestic service robots. His key research areas include RGB-D fusion for grasp planning, large-scale dataset creation, and the development of robust grasping strategies for challenging, everyday objects. Yoon’s most notable contribution is the "RGBD Fusion Grasp Network," a novel deep learning architecture that fuses color and depth information to predict stable grasps. This work is particularly significant for its application to tableware—objects like plates and bowls that are notoriously difficult for robots to handle due to their flat, reflective, and slippery surfaces. To support this research, he introduced a large-scale tableware grasp dataset, enabling the training of more generalizable and reliable grasping models. While his 2023 paper has already garnered early citations, its true impact lies in addressing a critical bottleneck in home robotics: the safe and efficient manipulation of everyday items. Yoon’s work is paving the way for robots that can assist with tasks like dishwashing and table setting, bringing us closer to truly helpful domestic assistants.