Abstract
Purpose of Review We discuss the use of differentiable simulation for computational problems in soft robotics. This includes characterizing the mechanical behavior of soft robots, optimally controlling embedded soft actuators or active materials, and estimating the robot’s state from readings of embedded sensors. Moreover, we discuss how design optimization can help to optimally place soft actuators and sensors. Recent Findings We expatiate on the adoption of simulation and optimization tools in the process of designing and controlling soft robots. We include a discussion of rigid-flexible systems and the use of differentiable simulation in combination with machine learning. Summary We review the state of the art in the computational modeling of soft robots and provide a summary of the required mathematical tools. We also review several open questions where computation could help to move the field forward, and discuss the role of differentiable simulation in managing the ever-growing design complexity of next-generation soft robots.
