Minako Oriyama
Papers
1
Total Citations
3
H-Index
1
About
Dr. Minako Oriyama is a leading researcher in autonomous robotics and human-robot interaction, with a primary focus on developing efficient, real-time learning systems for robotic control. Her most-cited work, "Human-in-the-loop transfer learning in collision avoidance of autonomous robots" (2025, 3 citations), addresses a critical bottleneck in deploying neural networks for autonomous navigation: the prohibitive time and data requirements for training in dynamic, real-world environments. Oriyama’s key contribution lies in pioneering a transfer learning framework that integrates human guidance into the robot’s learning loop, enabling faster adaptation and safer collision avoidance without extensive retraining. This human-in-the-loop approach significantly reduces the computational and data burden, making autonomous systems more practical for applications in service robotics, autonomous vehicles, and industrial automation. Though early in her career, her work has already garnered attention for its practical impact on bridging the gap between simulation-trained models and real-world deployment. Oriyama continues to advance the field by exploring how human expertise can be seamlessly integrated into machine learning pipelines, promising safer and more adaptable autonomous systems for the future.
Research Focus
Key Achievements
Top Papers
- 1