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About

Yugo Takeuchi is a researcher specializing in human-robot interaction, nonverbal communication, and social robotics, with a particular focus on how humans perceive and engage with robotic agents in natural social contexts. His work explores the subtle, often subconscious mechanisms that govern human interaction and investigates how these principles can be applied to robot behavioral design. Among his most notable contributions is his research into multi-party conversation management, where he demonstrated that a robot's gaze can effectively coordinate turn-taking dynamics in group settings — a finding that has garnered 13 citations and holds significant implications for social robotics and collaborative AI systems. Takeuchi has also made meaningful strides in affective computing, showing that humans readily attribute emotional states to simple autonomous robots based solely on movement patterns, drawing on Russell's circumplex model of affect. His broader research agenda examines the early, pre-conscious stages of human-artifact interaction — what he terms "subconscious embodied interaction" — and proposes agent models capable of generating naturalistic encounter behaviors. Collectively, his work bridges cognitive science, robotics, and communication theory, offering foundational insights for designing socially intelligent machines that integrate seamlessly into human environments.