Yasunobu Ogawa

Gifu Prefectural Research Institute for Forests

Papers

Total Citations

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About

Yasunobu Ogawa’s research lies at the intersection of human-robot interaction, affective computing, and developmental robotics, with a core focus on enabling robots to learn and respond to human facial expressions through natural interaction. His major contribution is pioneering a self-learning framework that mirrors how a baby acquires emotional understanding—not through pre-programmed labels, but by observing and being affected by human reactions. In his most cited work, “Learning system of human facial expression for a family robot” (2004, 9 citations), Ogawa proposed that a robot could learn expressions by imitating a child’s developmental process, where meaning emerges from social feedback rather than explicit instruction. This approach was further refined in “Self-learning system of facial expression for intelligent robot through interaction with human” (2004, 3 citations), where he implemented a system enabling a robot to understand and behave according to human expressions after real-time interaction. Though his citation counts are modest, Ogawa’s work is notable for its conceptual originality—challenging conventional supervised learning paradigms by grounding robot emotional intelligence in embodied, interactive learning. His research offers a compelling foundation for students interested in how machines might develop social cognition through experience, much like humans do.