Embodying Control in Soft Multistable Robots from Morphofunctional Co-design.

Soft robots are distinguished by their flexibility and adaptability, allowing them to perform nearly impossible tasks for rigid robots. However, controlling their behavior is challenging due to their nonlinear material response and infinite degrees of freedom. A potential solution to these challenges is to discretize their infinite-dimensional configuration space into a finite but sufficiently large number of functional modes with programmed dynamics. A strategy is presented for co-designing the desired tasks and morphology of pneumatically actuated soft robots with multiple encoded stable states and dynamic responses. This approach introduces a general method to capture the soft robots' response using an energy-based analytical model, the parameters of which are obtained using Recursive Feature Elimination. The resulting lumped-parameter model enables the inverse co-design of the robot's morphology and planned tasks by embodying specific dynamics upon actuation. This approach's ability to explore the configuration space is shown by co-designing kinematics with optimized stiffnesses and time responses to obtain robots capable of classifying the size and weight of objects and displaying adaptable locomotion with minimal feedback control. This strategy offers a framework for simplifying the control of soft robots by exploiting the mechanics of multistable structures and embodying mechanical intelligence into soft material systems.