Embodying Control in Soft Multistable Robots from Morphofunctional Co-design

TL;DR

This paper introduces a co-design strategy for soft multistable robots that simplifies control by embedding desired dynamics through an energy-based model, enabling versatile tasks like object classification and adaptable locomotion.

Contribution

It presents a novel method to co-design soft robot morphology and tasks using an energy-based analytical model and recursive feature elimination, capturing nonlinear responses effectively.

Findings

Successfully co-designed robots for object size and weight classification.

Demonstrated adaptable locomotion with minimal feedback control.

Validated the approach through simulation of multistable soft robots.

Abstract

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 the infinite-dimensional configuration space into a finite but sufficiently large number of functional modes with programmed dynamics. We present a strategy for co-designing the desired tasks and morphology of pneumatically actuated soft robots with multiple encoded stable states and dynamic responses. Our 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 facilitates inverse co-design of the robot's morphology and planned tasks by embodying specific dynamics upon actuation. We illustrate our approach's ability to explore the configuration space 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 nonlinear mechanics of multistable structures and embodying mechanical intelligence into soft material systems