Hyundo Kim

Goethe University Frankfurt, University of California San Diego

Papers

Total Citations

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About

Hyundo Kim’s research lies at the intersection of developmental robotics, computer vision, and human-robot interaction, with a focus on enabling robots to learn social and perceptual skills in real-world environments. His most influential work, “A robotic model of the development of gaze following” (2008, 20 citations), demonstrates a pioneering approach where a humanoid robot autonomously learns gaze following—a foundational social skill—by detecting salient objects and distinguishing human cues. This contribution is critical for advancing social robotics and imitation learning. Earlier, in “Semi-autonomous Learning of Objects” (2006, 16 citations), Kim developed a vision system that allows robots to recognize novel objects without manual labeling, streamlining the training process for robotic perception. His work on “Adaptive Object Tracking with an Anthropomorphic Robot Head” (2004, 4 citations) introduced a biologically inspired, self-organized fusion of visual cues for robust tracking. Collectively, Kim’s research has shaped how robots acquire social and perceptual abilities through minimal human intervention, making his contributions valuable for students and researchers in cognitive robotics and autonomous learning systems.