Deep Koopman Representation for Control over Images (DKRCI)

Abstract

The Koopman operator provides a means to represent nonlinear systems as infinite dimensional linear systems in a lifted state space. This enables the application of linear control techniques to nonlinear systems. However, the choice of a finite number of lifting functions, or Koopman observables, is still an unresolved problem. Deep learning techniques have recently been used to jointly learn these lifting function along with the Koopman operator. However, these methods require knowledge of the system’s state space. In this paper, we present a method to learn a Koopman representation directly from images and control inputs. We then demonstrate our deep learning architecture on a cart-pole system with external inputs.