Papers
15
Total Citations
78
H-Index
5
About
Kazuaki Ito is a Japanese robotics and human-machine interaction researcher whose work spans intelligent grasp classification, robotic control systems, and advanced motor design. His most prominent contributions center on leveraging multisensory data gloves and deep learning to decode human grasping behavior — research with transformative implications for prosthetics, exoskeletons, and rehabilitation engineering. His 2024 paper "Glove-Net," already accumulating 16 citations, introduces a deep learning framework that captures rich grasp dynamics through multisensory glove data, while companion studies exploring machine learning-based tactile signal interpretation and grasp synergies during daily living activities further cement his standing in this rapidly evolving field. Beyond grasp research, Ito has made noteworthy contributions to industrial robotics, including state feedback-based vibration suppression for multi-axis robot arms and iterative learning-based trajectory generation for force-sensitive tasks. His 2022 work on a radial-gap two-degree-of-freedom motor based on a magnetic screw structure demonstrates his range across electromechanical systems. With over 60 cumulative citations across a decade of publications, Ito's interdisciplinary research bridges biomechanics, control engineering, and artificial intelligence, making his work essential reading for researchers advancing human-centered robotics.
Research Focus
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Top Papers
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