Yuichiro Aoyama

Georgia Institute of Technology, Komatsu (Japan)

Papers

Total Citations

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About

Yuichiro Aoyama is a leading researcher in trajectory optimization and robust control, with a focus on advancing Differential Dynamic Programming (DDP) for real-world robotics. His major contributions center on extending DDP to handle constraints and parametric uncertainty—two critical challenges in autonomous systems. His seminal 2021 paper, "Constrained Differential Dynamic Programming Revisited" (34 citations), builds on penalty methods to develop a widely successful constrained DDP framework, addressing a long-standing gap in the field. This work has become a key reference for researchers seeking to apply DDP in complex, constraint-heavy environments. Aoyama further expanded the scope of DDP in his 2021 paper, "Receding Horizon Differential Dynamic Programming Under Parametric Uncertainty" (6 citations), where he integrates Generalized Polynomial Chaos (gPC) theory to propagate uncertainty and design robust control policies. By combining gPC with receding horizon optimization, his work enables DDP to handle system uncertainties effectively, paving the way for safer and more reliable autonomous navigation. With a growing citation impact, Aoyama’s research is shaping the next generation of optimization algorithms for robotics and control.