Shingo Kurita
Papers
1
Total Citations
6
H-Index
1
About
Shingo Kurita is a robotics researcher whose work bridges neural network theory and industrial automation. His most cited paper, "Neural Network-Based Inverse Kinematics for an Industrial Robot and Its Learning Method" (2016, 6 citations), addresses a critical bottleneck in robotic control: the time-intensive learning process of neural networks for inverse kinematics. Kurita’s key contribution lies in developing an adaptive learning method that prioritizes training on input-output pairs with the highest errors after initial iterations—such as 1000 epochs—thereby accelerating convergence and improving accuracy for industrial robots. This approach reduces computational overhead while enhancing real-time performance, making it valuable for manufacturing and automation. His research areas include neural network optimization, robot kinematics, and machine learning for control systems. Though his citation count is modest, his work demonstrates a focused impact on practical robotics challenges, offering a scalable solution for industries requiring precise, efficient robot motion planning. Kurita’s methodology has potential applications in collaborative robotics and adaptive control, marking him as a contributor to the evolution of intelligent industrial systems.
Research Focus
Key Achievements
Top Papers
- 1