Exploiting the Dynamics of Soft Materials for Machine Learning

Soft materials have been attracting attention because they add unprecedented functionality to machines and devices. This functionality enables soft materials to be used in a vast array of applications, such as grasping objects,1,2 human–robot interactions,3 medical and surgical tools,4 and prosthetics and wearables.5 The inherent softness of such materials results in increased adaptivity and decreased damage to other surfaces during contact.6,7 In addition, robots made with soft materials are able to generate complex behaviors with simpler actuations by partially outsourcing control to the morphological and material properties,8 which enhances the active coupling between control, body, and environment.9,10 Compared with rigid materials, soft materials exhibit rich dynamics including a variety of properties, such as nonlinearity, elasticity, and high dimensionality. In this article, we demonstrate that these dynamic properties constitute an asset that can be effectively employed for machine learning purposes. Our approach is based on a technique called reservoir computing,11–13 which is a framework rooted in recurrent neural network learning. When a high-dimensional dynamical system, which is referred to as the reservoir, is driven with input streams, it generates transient dynamics that operate as a type of temporal and finite kernel that facilitates the separation of the input states. If the dynamics involve short-term memory and nonlinear processing of the input stream, then nonlinear dynamical systems can be learned by adjusting a linear, static readout from the high-dimensional state space of the reservoir. We exploit the rich physical dynamics of soft materials directly as a reservoir for temporal machine learning problems.