Yunsick Sung
Papers
8
Total Citations
72
H-Index
6
About
Yunsick Sung is a leading researcher at the intersection of 3D computer vision, robotics, and human-robot interaction. His work primarily focuses on enabling autonomous systems to perceive, learn, and interact with their environments more intelligently. A major contribution is the development of DGCB-Net, a dynamic graph convolutional broad network for 3D object recognition in point clouds, which has garnered 15 citations and advances environment perception for mobile robots and disease diagnosis. Sung has also pioneered novel clustering methods for 3D point clouds using range-image-based density-based spatial clustering, cited 13 times, which is critical for autonomous perception tasks like object detection and classification in self-driving vehicles. Beyond perception, he has made significant strides in robot control and learning, including a genetic algorithm-based motion estimation method using EMG signals from wearable devices (12 citations) and collaborative programming by demonstration in virtual environments (12 citations). His work on graph-based motor primitive generation frameworks and human-robot interaction learning through Q-learning in pervasive sensing environments further demonstrates his impact. With over 70 total citations across his most-cited works, Sung’s research is shaping the future of autonomous robots, from UAVs to service robots in smart environments.
Research Focus
Key Achievements
Top Papers
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- 4Collaborative programming by demonstration in a virtual environment12 citations · 2012
- 5Graph-based motor primitive generation framework9 citations · 2015
- 6
- 7Reactive virtual agent learning for NUI-based HRI applications3 citations · 2014
- 8Robot Service Framework Based on Big Data Technology2 citations · 2014