Hyun Oh Song

Papers

Total Citations

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About

Hyun Oh Song is a leading researcher in computer vision and robotics, with a focus on enabling machines to perceive and interact with the physical world. His most cited work, "Learning to Detect Visual Grasp Affordance" (2015, 67 citations), introduces a groundbreaking framework for estimating grasp affordances directly from 2D visual data. This approach overcomes the limitations of 3D scans in cluttered or reflective environments by leveraging local texture features and object-category cues, allowing robots to identify where and how to grasp objects even under challenging conditions. Beyond this, Song has made significant contributions to deep learning for visual recognition and reinforcement learning, bridging perception and action. His work has been widely adopted in robotic manipulation and autonomous systems, earning recognition for its practical impact. With a citation count reflecting the influence of his research, Song continues to advance the frontier of intelligent robotics, making his work essential reading for students and researchers interested in the intersection of vision, learning, and physical interaction.