Papers
26
Total Citations
236
H-Index
8
About
Takato Horii is a robotics and artificial intelligence researcher whose work spans human-robot interaction, cognitive architectures, affective computing, and robotic sensing. He has made notable contributions to tactile sensing technology, most prominently through his development of a flexible tactile sensor using magnetorheological elastomers that produces a distinctive Mexican-hat-like response—a paper that has garnered 61 citations and addresses longstanding durability challenges in robotic touch. Beyond physical sensing, Horii has deeply explored how robots can understand and express emotion, proposing unified models for emotional estimation and expression using multimodal Boltzmann machines, and extending this work to active inference frameworks for affective human-robot interaction. His research on cognitive architectures integrates action planning and language understanding, while more recent contributions leverage large language models for multi-robot task planning, reflecting his engagement with cutting-edge AI. Particularly distinctive is his work on explainable AI applied to childcare robotics, enabling robots to estimate toddler temperament transparently—a socially impactful direction. With publications spanning perception, cognition, emotion, and explainability, Horii represents a researcher committed to building robots that are not only capable, but trustworthy and socially aware partners for humans.
Research Focus
Key Achievements
Top Papers
- 1
- 2Integrated Cognitive Architecture for Robot Learning of Action and Language24 citations · 2019
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- 6Decision-Making in Emotion Model9 citations · 2018
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- 8A framework of explanation generation toward reliable autonomous robots8 citations · 2021
- 9Explainable Temperament Estimation of Toddlers by a Childcare Robot8 citations · 2020
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