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About

Maina Sogabe is a rising researcher at the forefront of soft robotics and intelligent assistive systems, whose work is redefining how machines interact with the human body. Sogabe’s primary research areas include soft pneumatic actuation, hysteresis compensation, and wearable gait assistance, with a growing focus on integrating machine learning for surgical automation. A key contribution is the development of an adaptive control method for dual-PAM soft actuators that compensates for hysteresis—a persistent challenge in soft robotics—enabling more precise trajectory tracking (13 citations). Sogabe also pioneered a novel gait assistive suit that uses physical reservoir computing to exploit air dynamics, eliminating the need for bulky electrical sensors and computers (12 citations). This work demonstrates how soft, pneumatic components can serve dual roles as both actuators and sensors, significantly enhancing wearer mobility. Further notable achievements include applying deep learning to estimate future needle positions during suturing for semi-autonomous surgical robots, and using clustering of pressure information for posture estimation in pneumatically driven gait-assist robots. With a clear trajectory toward safer, more intuitive human-robot interaction, Sogabe’s research is poised to impact rehabilitation, surgery, and beyond.