Embedded soft sensors can significantly impact the design and control of soft-bodied robots. Although there have been considerable advances in technology behind these novel sensing materials, their application in real-world tasks, especially in closed-loop control tasks, has been severely limited. This is mainly because of the challenge involved with modeling a nonlinear time-variant sensor embedded in a complex soft-bodied system. This article presents a learning-based approach for closed-loop force control with embedded soft sensors and recurrent neural networks (RNNs). We present learning protocols for training a class of RNNs called long short-term memory (LSTM) that allows us to develop accurate and robust state estimation models of these complex dynamical systems within a short period of time. Using this model, we develop a simple feedback force controller for a soft anthropomorphic finger even with significant drift and hysteresis in our feedback signal. Simulation and experimental studies are conducted to analyze the capabilities and generalizability of the control architecture. Experimentally, we are able to develop a closed-loop controller with a control frequency of 25?Hz and an average accuracy of 0.17?N. Our results indicate that current soft sensing technologies can already be used in real-world applications with the aid of machine learning techniques and an appropriate training methodology.

Soft sensing technologies have the potential to revolutionize wearable devices, haptic interfaces and robotic systems. However, there are numerous challenges in the deployment of these devices due to their poor resilience, high energy consumption, and omnidirectional strain responsivity. This work reports the development of a versatile ionic gelatin-glycerol hydrogel for soft sensing applications. The resulting sensing device is inexpensive and easy to manufacture, is self-healable at room temperature, can undergo strains of up to 454%, presents stability over long periods of time, and is biocompatible and biodegradable. This material is ideal for strain sensing applications, with a linear correlation coefficient R2 = 0.9971 and a pressure-insensitive conduction mechanism. The experimental results show the applicability of ionic hydrogels for wearable devices and soft robotic technologies for strain, humidity, and temperature sensing while being able to partially self-heal at room temperature.

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.