Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

软机器人由于其固有的安全性嵌入了材料级别，因此已引起了现实应用的兴趣。这些机器人使用能够变形和改变行为的可变形材料，并允许适当的物理接触进行操纵。然而，随着将柔软且可拉伸的材料引入机器人系统，传感器集成面临许多挑战，包括能够拉伸的多模态传感，高分辨率但大面积传感器阵列的嵌入以及随着数据量的增加而融合的传感器。本评论探讨了电子皮肤与机器学习的融合趋势，重点关注机器人专家如何结合两个领域的最新发展成果，以构建自主的，可部署的软机器人，