AUTHOR=Yang Haitao , Wu Wenbo 

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

JOURNAL=Frontiers in Electronic Materials

VOLUME=Volume 2 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/electronic-materials/articles/10.3389/femat.2022.1000781

DOI=10.3389/femat.2022.1000781

ISSN=2673-9895

ABSTRACT=Compliant and soft strain sensors that detect machinal deformations become prevalent in emerging soft machines for closed-loop feedback control. In contrast to conventional sensing applications, the stretchy robotic body of soft machines 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. In this perspective, we summarize the latest advancement of sensor-integrated soft machines design driven by artificial intelligence techniques, including sensor materials optimization, sensor signal analyses, and in-sensor computing. We also discuss the prospects of fusing artificial intelligence and soft sensing technology for creating next-generation intelligent soft machines.