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About

Yong-Deuk Shin’s research lies at the intersection of robotic manipulation, perception, and human-robot interaction, with a focus on enabling robots to perform dexterous, real-world tasks. His most-cited work, “Robotic Peg-in-Hole Assembly by Hand Arm Coordination” (2015, 8 citations), addresses a classic industrial challenge by integrating coordinated arm and hand motion for precision assembly. Shin has also made significant contributions to rescue robotics, developing an interactive remote operation framework (2013, 6 citations) that enhances operator situation awareness in disaster environments filled with obstacles and confined passages. In object manipulation, he proposed a 6DOF pose estimation method using 2D-3D sensor fusion (2012, 4 citations), leveraging SURF descriptors to bridge image and point cloud data. His work on multi-fingered robot hands includes a human-inspired grasping planning framework (2011, 3 citations) that decomposes grasping into type, opposition parameter, and approach vector planning, as well as an integrated recognition and planning system (2013, 3 citations) that adapts grasping strategies based on object type and pose. Though his citation counts are modest, Shin’s research systematically tackles core problems in robotic dexterity and perception, laying foundational work for more autonomous and capable robotic systems.