Leveraging Morphological Computation for Controlling Soft Robots: Learning from Nature to Control Soft Robots

Traditional robot designs typically employ rigid body parts and high-torque servo motors. This helps to obtain simple reproducible models and, therefore, to facilitate control. Soft robotics is a new research field that deliberately expands the design toolbox to a wide range of smart and, often, soft materials. This approach is generally inspired by the remarkable performance of biological systems, which use soft structures to interact successfully with noisy and hard-to-model environments and, as a result, outperform state-of-the-art robots in open-world scenarios. However, using soft bodies comes with a significant disadvantage. Soft materials often have complex and nonlinear dynamics, which makes them hard to model and therefore difficult to control. To fulfill the potential of soft robotics to achieve performances close to biological systems, this control problem has to be solved. A promising solution is another bioinspired principle called morphological computation, which proposes to outsource functionality directly to the body morphology. From this point of view, the seemingly undesired nonlinear dynamics become a resource for implementing nonlinear functionalities. This extends the control design problem to the question of how to design the body morphology of the robot. While there exist proofs of concept that demonstrate the potential of this approach, the existing work (for the most part) is lacking mathematical rigor and a general framework. We believe that the control community has the right set of tools to support the development of a design framework for morphological computation. The goal of this article (see “Summary”) is to provide an introduction to the concepts of soft robotics and morphological computation, explain how they can work together, and (with the help of examples) illustrate their potential for control. The hope is to inspire members of the control community to develop novel control frameworks for the next generation of soft robots.