Taku Sugiyama
Papers
3
Total Citations
15
H-Index
2
About
Taku Sugiyama is a leading researcher in the field of soft robotics, with a primary focus on the control, actuation, and sensing of pressure-driven soft actuators (PSAs). His work is pivotal in overcoming the fundamental challenges that prevent soft robots from transitioning from lab prototypes to practical, real-world applications. Sugiyama’s major contributions include pioneering a latent representation-based learning controller that enables the seamless dual actuation of PSAs using both pneumatic and hydraulic power—a breakthrough that addresses the vastly different physical characteristics of air and water. He has also developed an iterative learning-based neural network to compensate for the individual deformability of soft hydraulic actuators, a critical step for reliable rehabilitation robotics. Furthermore, his recent work on a versatile graceful degradation framework for bio-inspired proprioception tackles the fragility of soft sensors, ensuring robust feedback even when sensors are damaged. With his most-cited papers accumulating citations in the single digits, Sugiyama’s impact is measured not by volume but by the foundational nature of his contributions, which are essential for the future of intelligent, resilient soft robots.
Research Focus
Key Achievements
Top Papers
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