Masaru Hatano
Papers
1
Total Citations
2
H-Index
1
About
Masaru Hatano’s research centers on the intersection of robotics, computer vision, and neural networks, with a particular focus on enhancing machine perception for precise robotic control. His most cited work, “Structured lighting to enhance global image feature sensitivity in a neural network based robot-positioning task” (2002), introduced an innovative method for visually guiding a 5-DOF robot arm. By projecting a structured grid pattern onto target surfaces, Hatano created artificial global image features that significantly improved a neural network’s ability to interpret spatial information. This approach allowed the robot to achieve accurate positioning without relying on complex local feature detection, demonstrating a clever fusion of hardware-based lighting techniques with adaptive learning algorithms. Although his citation count remains modest, Hatano’s contribution is notable for its practical elegance—simplifying a traditionally difficult perception problem through engineered illumination. His work offers a valuable lesson in how thoughtful experimental design can amplify the capabilities of neural systems, making it a relevant reference for researchers exploring sensorimotor integration and vision-guided robotics.
Research Focus
Key Achievements
Top Papers
- 1