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About

Mikio Nakano is a pioneering researcher whose work bridges human-robot interaction, robot audition, and conversational AI. His research spans several interconnected domains, including robust speech recognition for robots, dialogue and behavior planning, multimodal perception, and the design of intuitive communication cues between humans and machines. Among his most influential contributions is his development of real-time robot audition systems capable of recognizing simultaneous speech in noisy, real-world environments — work that has garnered over 60 citations and addressed one of the field's most persistent technical challenges. His two-layer model for behavior and dialogue planning in conversational service robots (43 citations) laid important groundwork for enabling robots to accurately interpret human intentions during complex interactions. Nakano also introduced the concept of "artificial subtle expressions" (ASEs) — audio and visual cues that communicate a robot's internal states to users — significantly advancing the naturalness of human-robot communication. His exploration of social dialogue's role in trust repair during robot conversational errors (57 citations) reflects his broader interest in the social dimensions of robotic interaction. Further contributions in multimodal object categorization demonstrate his commitment to enabling robots to autonomously learn from their environments, marking him as a versatile and impactful figure in intelligent robotics research.