Papers
4
Total Citations
17
H-Index
2
About
Katsumi Nitta is a pioneering researcher at the intersection of social robotics and human-robot interaction, with a focus on enabling machines to perceive and respond to human social signals. His most impactful work centers on developing interview robots capable of recognizing a person's speaking willingness through multimodal sensing—integrating verbal, audio, and visual cues. In his highly cited 2023 study, Nitta introduced an adaptive interview strategy that allows robots to dynamically adjust their questioning based on real-time recognition of an interviewee's internal state, achieving 7 citations. Earlier foundational work in 2018 established a prediction model for willingness in human-robot interviews, contributing a novel multimodal interaction corpus. Nitta’s research also explores robotic social imitation, where he proposed learning methods that enable robots to select behavioral patterns based on self-embodiment and self-evaluation, even when taught by multiple instructors with differing approaches. His work on DP matching and clustering for classifying teaching demonstrations has advanced robot learning under varied instruction. With a career spanning from embodied cognition to real-time social signal processing, Nitta’s contributions are shaping the next generation of socially aware conversational robots.
Research Focus
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