Akihiro Doi

Nara Institute of Science and Technology

Papers

Total Citations

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About

Akihiro Doi is a researcher specializing in control systems and machine learning, with a particular focus on adaptive and learning-based approaches for robotic and industrial applications. His most cited work, "Feedforward Learning Control by Scheduled Locally Weighted Regression" (2013), introduces a novel framework that combines locally weighted regression with scheduled learning to improve feedforward control in nonlinear systems. This contribution addresses key challenges in real-time adaptation and precision, offering a computationally efficient method for systems with varying dynamics. With 7 citations, this paper has influenced subsequent research in learning control, particularly in robotics and automation. Doi’s work bridges the gap between traditional control theory and data-driven techniques, emphasizing practical implementation and robustness. His research is notable for its focus on scheduled learning, which allows controllers to adapt to changing conditions without extensive retraining. For students and researchers in control engineering and machine learning, Doi’s contributions provide a foundation for developing intelligent, adaptive systems that learn from experience, making his work a valuable reference in the evolving field of learning-based control.