Yuichiro Yoshikawa

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About

Yuichiro Yoshikawa is a researcher whose work sits at the intersection of robotics, developmental science, and human-robot interaction. His research draws heavily on understanding how humans — particularly infants — develop fundamental social and communicative behaviors, and then translating these insights into functional robotic systems. One of Yoshikawa's most notable contributions is his investigation of joint attention, the foundational social behavior through which individuals coordinate focus on shared objects. His 2007 paper, which has accumulated 24 citations, introduced an innovative approach using transfer entropy as a measure of causality to enable robots to autonomously acquire joint attention behaviors — moving beyond static models to embrace genuine developmental learning. Complementing this work, his research on vowel imitation learning addresses the realistic, messy dynamics of caregiver-infant vocal interaction. Rather than assuming idealized conditions, Yoshikawa explored how a robotic "infant" could recognize when it is being imitated and adapt its articulation accordingly — a subtle but critical step toward authentic human-robot communicative development. Together, these contributions reflect Yoshikawa's broader ambition: to build robots that don't merely simulate social behavior, but develop it through interaction, mirroring the rich, contingent processes that shape human communication from infancy.