Papers
91
Total Citations
1,564
H-Index
21
About
Takayuki Nagai is a pioneering robotics and cognitive systems researcher whose work sits at the intersection of symbol emergence, multimodal learning, and human-robot interaction. Best known for his landmark survey on symbol emergence in robotics (2016, 153 citations), Nagai has dedicated his career to understanding how robots can autonomously develop language, concepts, and semiotic capabilities through physical interaction with their environment — mirroring processes observed in human cognitive development. A central thread of Nagai's research involves multimodal object categorization, where robots integrate visual, auditory, and haptic information to form unsupervised conceptual representations. His development of multimodal Latent Dirichlet Allocation (LDA) frameworks enabled robots to ground word meanings in real-world sensory experience, earning dozens of citations across multiple publications spanning 2007 to 2014. His SERKET architecture further advanced the field by enabling large-scale probabilistic cognitive models to be modularly constructed and deployed. More recently, Nagai has turned attention to explainability in autonomous robots (2022, 82 citations) and the frontiers of language acquisition in robotics, reflecting a broader commitment to safe, interpretable, and communicative AI systems. His sustained contributions across two decades make him an essential reference for researchers in developmental robotics and embodied cognition.
Research Focus
Key Achievements
Top Papers
- 1Symbol emergence in robotics: a survey153 citations · 2016
- 2Explainable autonomous robots: a survey and perspective82 citations · 2022
- 3Grounding of word meanings in multimodal concepts using LDA78 citations · 2009
- 4Multimodal object categorization by a robot77 citations · 2007
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- 7Survey on frontiers of language and robotics53 citations · 2019
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- 10Bag of multimodal LDA models for concept formation38 citations · 2011