Yonghyeok Seo
Papers
2
Total Citations
36
H-Index
2
About
Yonghyeok Seo is a robotics researcher whose work sits at the intersection of computer vision and robotic manipulation, with a primary focus on real-time, highly accurate grasp detection for novel objects. His most impactful contribution, a 2018 paper on using fully convolutional neural networks (FCNNs) with high-resolution images, achieved a remarkable 96.1% grasp detection accuracy and has garnered 25 citations, establishing a foundation for efficient robotic interaction in unstructured environments. Seo further advanced the field by integrating object detection with reasoning and grasp detection into a single multi-task deep neural network, a 2020 work that earned 11 citations. This approach enables robots not only to identify and grasp objects but also to reason about their spatial relationships, significantly expanding the practical utility of robotic systems. By tackling the dual challenges of speed and precision, Seo’s research directly addresses the bottleneck of real-world robotic deployment, offering solutions that are both computationally efficient and highly reliable. His work is particularly notable for bridging low-level perception with high-level reasoning, a critical step toward more autonomous and capable robotic assistants.
Research Focus
Key Achievements
Top Papers
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- 2