Ken Onozato
Papers
1
Total Citations
9
H-Index
1
About
Ken Onozato is a robotics researcher whose work focuses on the intersection of neural network learning and robotic control systems. His most significant contribution lies in developing innovative approaches to robot position control, particularly demonstrated in his highly cited 2007 paper on SCARA robots. In this foundational work, Onozato pioneered the use of two separate neural networks—one for inverse kinematics and one for inverse dynamics—to achieve precise position control. By employing the simultaneous perturbation method for network learning, he created a more efficient and practical approach to robotic arm manipulation. This work has accumulated 9 citations, establishing him as a contributor to the field of neural network-based robotics control. Onozato's research addresses fundamental challenges in making robots learn complex movements more effectively, bridging the gap between theoretical neural network applications and real-world robotic systems. His methodology offers a template for how multiple neural networks can work in concert to solve distinct but interconnected problems in robotics, making his work valuable for researchers exploring adaptive control systems and intelligent robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1