The soft robotics field is on the rise. The highly adaptive robots provide the opportunity to bridge the gap between machines and people. However, their elastomeric nature poses significant challenges to the perception, control, and signal processing. Hydrogels and machine learning provide promising solutions to the problems above. This review aims to summarize this recent trend by first assessing the current hydrogel-based sensing and actuation methods applied to soft robots. We outlined the mechanisms of perception in response to various external stimuli. Next, recent achievements of machine learning for soft robots’ sensing data processing and optimization are evaluated. Here we list the strategies for implementing machine learning models from the perspective of applications. Last, we discuss the challenges and future opportunities in perception data processing and soft robots’ high level tasks.

Multimodal sensing is crucial for soft robots’ environmental interaction and closed‐loop control. The sensing signals of mechanical deformation and temperature changes are often hard to differentiate manually. Herein, two hydrogel sensors are used for simultaneously proprioceptive, thermoceptive, and mechanoreceptive sensing. These stretchable sensors show high sensitivity to strain and temperature changes. Then, a machine learning model composed of a 1D convolutional neural network and a feed‐forward neural network is utilized to decode the sensing signal for various stimuli identification. It is demonstrated that the proposed method can accurately predict the soft actuator's body posture changes, such as bending, twisting, and stretching. In addition, the model can discern contact events with or without thermal stimuli. This data‐driven method for multimodal sensing discrimination might pave the way for future intelligent soft robots.