Hidetaka Ito

Papers

Total Citations

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About

Hidetaka Ito is a researcher in robotics and neural computation, whose work focuses on bridging artificial neural networks with physical robot control and biological motor pattern generation. His most cited paper, "Robot control using high dimensional neural networks" (2014, 9 citations), proposes a novel position control scheme for actual robot systems, employing complex-valued and quaternion neural networks to learn inverse kinematics for both 2D SCARA and 3D robotic arms. Ito has also explored self-organizing maps (SOM) for vector recognition, investigating how grouping affects classification performance in pattern recognition applications (2016, 4 citations). A notable contribution is his work on central pattern generators (CPG), where he models networks of neural oscillators capable of generating complex waveforms for rhythmic motion, with learning achieved via the simultaneous perturbation method (2012, 3 citations). Earlier in his career, Ito developed the IRI1 intelligent mobile robot (2002, 2 citations), a modular system combining memory-based neural network modules for learning with a random module for exploratory behavior. While his citation counts are modest, Ito’s research represents foundational work in applying high-dimensional neural networks to real-world robotic control and bio-inspired locomotion systems.