Papers
9
Total Citations
46
H-Index
4
About
Toru Yamamoto is a Japanese researcher whose work spans robotics education, mobile robot navigation, and intelligent control systems. He is perhaps best known for pioneering the concept of "Robo-iku" — education facilitated by robots — a framework designed to engage kindergarten through junior high school students with science and technology at a formative age. His most-cited work (12 citations) outlines this approach as a unified strategy to combat the growing avoidance of STEM learning among Japanese youth, complemented by related studies on rescue robot contests and "monozukuri" (hands-on engineering) activities in elementary schools. On the technical side, Yamamoto has made meaningful contributions to mobile robotics, developing vision-based navigation systems for omni-directional robots and applying reinforcement learning to dynamic path planning in unpredictable environments — each garnering seven citations. His work on CMAC-based online learning for path tracking further demonstrates his interest in adaptive control systems. More recently, he has extended his expertise into industrial applications, including a database-driven quality prediction system for painting robots (2022). Across his career, Yamamoto has bridged theoretical robotics research with practical educational and industrial impact, making him a distinctive voice in both fields.
Research Focus
Key Achievements
Top Papers
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- 5A Practice of Rescue Robot Contest in Junior High Schools4 citations · 2010
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- 8Design of a Database-Driven Quality Predictor for Painting Systems2 citations · 2022
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