About

Yukie Nagai is a pioneering researcher in developmental robotics and cognitive science, whose work bridges human infant development and artificial intelligence to advance our understanding of how intelligent behavior emerges. Best known for her groundbreaking 2003 paper on joint attention — which has garnered over 212 citations — Nagai demonstrated how robots can acquire fundamentally human social capabilities through embedded visual attention mechanisms and self-evaluative learning. Her research consistently draws inspiration from parent-infant interaction, as seen in her influential investigations into "Motionese," the specialized motion caregivers use when demonstrating actions to infants, which she computationally modeled to scaffold robot action learning. Nagai has made significant contributions to human-robot collaboration, exploring when and how robots should take initiative during shared tasks. Her 2021 work on world model learning reflects her broader ambition to unlock the principles of general-purpose intelligence through predictive learning frameworks. With multiple papers exceeding 80 citations and participation in premier venues like the ACM/IEEE HRI conference, Nagai's interdisciplinary contributions have profoundly shaped developmental robotics, making her an essential voice for researchers studying embodied cognition, imitation learning, and socially intelligent machines.

Research Focus

Key Achievements

21
H-Index
68
Papers
1,720
Total Citations
25
Avg Citations/Paper
🏆 Most Cited Paper
A constructive model for the development of joint attention
212 citations · 2003
📈 Most Prolific Year: 2016 (8 Papers)
🤝 Key Collaborators: 89
🏛 Institutions: The University of Osaka, Bielefeld University, The University of Tokyo, Hochschule Bielefeld, National Institute of Information and Communications Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 34 days ago