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About

Takafumi Sakamoto is a researcher specializing in human-robot interaction, embodied communication, and affective computing, with a particular focus on the subtle, subconscious dynamics that emerge between humans and autonomous agents. His work explores how people form relationships with unfamiliar artifacts and robotic systems, examining the foundational stages of interaction that precede conscious engagement. Among his most notable contributions is his 2015 study on emotional inference from robot movement, which demonstrated that humans can perceive and attribute affective states — mapped according to Russell's circumplex model — to a simple, flat robot navigating a floor. This nuanced finding, garnering 7 citations, highlights the profound sensitivity humans bring to interpreting even minimal motion cues. Sakamoto has further advanced the field by developing agent models that govern approach and avoidance behaviors based on spatial relationships and internal states, offering a scenario-independent framework for naturalistic encounter dynamics. His theoretical work on subconscious embodied interaction provides a valuable lens for understanding how agency identification unfolds before deliberate communication begins — a critical consideration for designing robots that humans can intuitively and comfortably engage with. Sakamoto's research collectively informs more empathetic, behaviorally intelligent robotic systems.