A review: Machine learning for strain sensor-integrated soft robots - Yang, Haitao; Wu, Wenbo (Frontiers in Electronic Materials, 2022)
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.