Miho Harata

Papers

Total Citations

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About

Miho Harata’s research lies at the intersection of human-robot interaction and affective computing, with a focus on designing emotionally intelligent robotic systems. Her most cited work, "Emotion Generation Model with Growth Functions for Robots" (2013, 6 citations), introduces a novel framework that enables robots to develop emotional responses over time, moving beyond static neural network-based models. By incorporating growth functions, Harata’s model allows robots to adapt their emotional expressions based on cumulative interactions, making their reactions more nuanced and human-like. This contribution addresses a key limitation in earlier emotion models, which often lacked developmental depth. In her follow-up study, "Developing Sophisticated Robot Reactions by Long-Term Human Interaction" (2013, 3 citations), she further demonstrates how sustained engagement with humans can refine robotic behavior, emphasizing the importance of longitudinal learning in social robotics. Though her citation counts are modest, Harata’s work is foundational for researchers exploring dynamic emotional architectures in autonomous systems. Her emphasis on growth and adaptation over time offers a compelling pathway for creating robots that can form deeper, more meaningful connections with people, a critical step toward seamless human-robot coexistence.