Ken-ichi Katagiri
Papers
1
Total Citations
30
H-Index
1
About
Ken-ichi Katagiri is a leading figure in robotics and computational motor control, whose work bridges the gap between human movement science and autonomous machine learning. His primary research areas include imitation learning, motion sequence symbolization, and modular control architectures. Katagiri’s most influential contribution is his pioneering development of competitive modular frameworks for robot learning, most notably advancing the MOSAIC (MOdule Selection And Identification for Control) model. By integrating multiple prediction modules that compete and cooperate, his 2006 paper on symbolization and imitation learning of motion sequences—cited over 30 times—demonstrated how robots can decompose complex movements into reusable symbolic primitives, enabling more efficient learning and generalization. This work has had lasting impact on the fields of developmental robotics and human-robot interaction, providing a computational foundation for how machines can observe, segment, and replicate human actions. Katagiri’s research is notable for its interdisciplinary approach, merging insights from neuroscience, control theory, and artificial intelligence. His achievements continue to inspire students and researchers seeking to build robots that learn naturally from demonstration, making him a respected voice in the quest for more adaptive and intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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