Daiki Maeno
Papers
1
Total Citations
6
H-Index
1
About
Daiki Maeno is a robotics researcher whose work centers on the control of flexible manipulator systems, with a particular focus on integrating reinforcement learning to address the inherent challenges of vibration and position accuracy. His most-cited paper, "Vibration and Position Control of a Two-Link Flexible Manipulator Using Reinforcement Learning" (2023), tackles the critical trade-off between lightweight, energy-efficient robot design and the destabilizing oscillations that arise from structural flexibility. By applying reinforcement learning algorithms, Maeno has contributed a novel approach to simultaneously suppress vibration and maintain precise positioning—a problem that has long limited the practical deployment of flexible manipulators in high-speed industrial applications. Though his citation count is still growing, his work is gaining traction as industries push toward cost-effective, high-performance automation. Maeno’s research sits at the intersection of control theory, machine learning, and mechanical design, offering a promising path toward next-generation robots that are both agile and accurate.
Research Focus
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Top Papers
- 1