Takeshi Furuhashi
Papers
36
Total Citations
652
H-Index
11
About
Takeshi Furuhashi is a Japanese researcher whose work spans robotics, neural networks, fuzzy systems, and human-robot interaction. He is perhaps best known for his pioneering contributions to neural network-based control of robotic manipulators in the early 1990s. Rather than using neural networks to model inverse dynamics, Furuhashi's landmark approach employed them to compensate for nonlinearities and uncertainties in robotic systems — a conceptually elegant distinction that earned his 1991 paper over 214 citations and his 1992 follow-up more than 100. This body of work helped establish neural network compensation as a viable alternative to conventional adaptive control schemes. Beyond control theory, Furuhashi made significant contributions to evolutionary and fuzzy computing, developing genetic-based machine learning approaches — including the "Nagoya approach" — for discovering efficient fuzzy rules in complex robotic environments. In later years, his research shifted toward socially intelligent robotics, exploring how emotional expression and sympathy-modeling can sustain student engagement with educational-support robots. His studies on collaborative learning with robots have drawn growing interest, reflecting an impressive breadth of vision across decades of research. Furuhashi's career exemplifies a rare trajectory from low-level control engineering to the nuanced psychology of human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Trajectory control of robotic manipulators using neural networks214 citations · 1991
- 2A neural network compensator for uncertainties of robotics manipulators106 citations · 1992
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- 4An Emotional Expression Model for Educational-Support Robots35 citations · 2015
- 5A neural network compensator for uncertainties of robotic manipulators33 citations · 1990
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- 9A Fuzzy Classifier System for evolutionary learning of robot behaviors18 citations · 1998
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