Dong-Kyo Jeong

Papers

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About

Dong-Kyo Jeong is a researcher at the intersection of robotics and artificial intelligence, specializing in computer vision, tactile sensing, and autonomous manipulation. His work focuses on enabling robots to perceive and interact with their environments more intelligently. In his most cited paper, "Mask-RCNN based object segmentation and distance measurement for Robot grasping" (2019, 7 citations), Jeong proposed a novel application that leverages Mask-RCNN to accurately segment target objects from cluttered backgrounds and measure their distance, directly improving robotic grasping precision. This contribution addresses a critical challenge in real-world robotics: reliable object extraction in unstructured settings. Additionally, his work on "Artificial Neural Network Based Tactile Sensing Unit for Robotic Hand" (2019, 2 citations) explores integrating neural networks into tactile sensors, enhancing a robotic hand’s ability to sense and respond to physical contact. Though early in his career, Jeong’s research demonstrates a clear trajectory toward creating more perceptive and dexterous robotic systems, with potential applications in manufacturing, healthcare, and service robotics. His work underscores the growing importance of combining deep learning with robotic hardware for practical, real-world impact.