Morphological computation: The good, the bad, and the ugly

In many robotic applications, softness leads to improved performance, robustness, and safety, while lowering manufacturing cost, increasing versatility, and simplifying control. The advantages of soft robots derive from the fact that their behavior partially results from interactions of the robot's morphology with its environment, which is commonly referred to as morphological computation (MC). But not all MC is good in the sense that it supports the desired behavior. One of the challenges in soft robotics is to build systems that exploit the morphology (good MC) while avoiding body-environment interactions that are harmful with respect to the desired functionality (bad MC). Up to this point, constructing a competent soft robot design requires experience and intuition from the designer. This work is the first to propose a systematic approach that can be used in an automated design process. It is based on calculating a low-dimensional representation of an observed behavior, which can be used to distinguish between good and bad MC. We evaluate our method based on a set of grasping experiments, with variations in hand design, controller, and objects. Finally, we show that the information contained in the low-dimensional representation is comprehensive in the sense that it can be used to guide an automated design process.