Shinsuke Akizuki
Papers
2
Total Citations
9
H-Index
2
About
Shinsuke Akizuki is a robotics researcher whose work focuses on the control of multi-joint robotic systems, particularly through innovative feedforward torque generation methods. His key research areas include iterative learning control, motion-scale transformation, and basis-motion torque composition—techniques that enable precise joint-trajectory tracking without relying solely on real-time feedback. Akizuki’s major contributions lie in developing methods that reuse and compose time-series torque data from learned motions, allowing robots to adapt to arbitrary postures and complex movements. His most cited paper, “Posture control of a multi-joint robot based on composition of feedforward joint-torques acquired by iterative learning” (2009, 6 citations), introduces motion-scale transformation for a two-DOF planar robot arm, while his follow-up work on basis-motion torque composition (2009, 3 citations) extends this approach to serially linked arms through arithmetic operations on torque data. Though his citation counts are modest, Akizuki’s work represents a foundational step toward efficient, data-driven robot control, offering practical solutions for reducing computational burden in trajectory planning. His research is particularly valuable for students and engineers exploring learning-based control in robotics, demonstrating how past motion data can be repurposed to achieve new, precise movements.
Research Focus
Key Achievements
Top Papers
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- 2