Papers
9
Total Citations
555
H-Index
6
About
Masato Ito is a pioneering researcher in cognitive robotics, whose work centers on the intersection of neural network models, mirror neuron systems, and human-robot interaction. His major contributions lie in developing the Recurrent Neural Network with Parametric Bias (RNNPB) model, which enables robots to self-organize and dynamically generate multiple behavior schemata through imitation and interaction. This framework, inspired by the human mirror system and parietal cortex functions, allows humanoid robots to learn, switch, and adapt object handling behaviors in real time. Ito’s most influential work, "Self-organization of distributedly represented multiple behavior schemata in a mirror system" (2004), has garnered over 216 citations, establishing a foundational approach for embodied cognition in robotics. His subsequent studies on on-line imitative interaction (114 citations) and dynamic behavior generation (126 citations) demonstrate how robots can engage in codevelopmental learning with human tutors, fostering joint attention and turn-taking. These achievements have not only advanced the field of developmental robotics but also provided a computational framework for understanding social learning mechanisms. Ito’s research continues to inspire new generations of roboticists and cognitive scientists, bridging the gap between neural modeling and real-world autonomous behavior.
Research Focus
Key Achievements
Top Papers
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- 5Joint attention between a humanoid robot and users in imitation game21 citations · 2004
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