Keiichi Kurokawa
Papers
2
Total Citations
10
H-Index
2
About
Keiichi Kurokawa is a pioneering figure in the integration of neural networks with robotic control systems. His research focuses on the calibration and error compensation of robot manipulators, addressing fundamental challenges in precision and accuracy for industrial automation. Kurokawa's seminal 1992 paper, "Calibration of position and orientation of robot manipulators using a neural network," introduced a novel approach that leverages neural networks to correct positional and orientational errors arising from modeling inaccuracies or setup misalignments. This work, along with his 1993 follow-up study, demonstrated how neural networks could learn and compensate for systematic errors by modifying reference inputs to robot controllers. Though his most-cited papers have accumulated modest citation counts—7 and 3 respectively—their conceptual impact is significant, laying early groundwork for the now-ubiquitous use of machine learning in robotics calibration. Kurokawa's contributions are particularly notable for their foresight, anticipating the critical role of adaptive, data-driven methods in achieving high-precision robotic manipulation. His research remains a touchstone for engineers and researchers developing intelligent, self-correcting robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2