A review: Machine learning for strain sensor-integrated soft robots

Abstract

Compliant and soft sensors that detect machinal deformations become prevalent in emerging soft robots for closed-loop feedback control. In contrast to conventional sensing applications, the stretchy body of the soft robot enables programmable actuating behaviors and automated manipulations across a wide strain range, which poses high requirements for the integrated sensors of customized sensor characteristics, high-throughput data processing, and timely decision-making. As various soft robotic sensors (strain, pressure, shear, etc.) meet similar challenges, in this perspective, we choose strain sensor as a representative example and summarize the latest advancement of strain sensor-integrated soft robotic design driven by machine learning techniques, including sensor materials optimization, sensor signal analyses, and in-sensor computing. These machine learning implementations greatly accelerate robot automation, reduce resource consumption, and expand the working scenarios of soft robots. We also discuss the prospects of fusing machine learning and soft sensing technology for creating next-generation intelligent soft robots.