Smart Forgetting for Safe Online Learning with Gaussian Processes

Abstract

The identification of unknown dynamical systems using supervised learning enables model-based control of systems that cannot be modeled based on first principles. While most control literature focuses on the analysis of a static dataset, online learning control, where data points are added while the controller is running, has rarely been studied in depth. In this paper, we present a novel approach for online learning control based on Gaussian process models. To avoid computational difficulties with growing datasets, we propose a safe forgetting mechanism. Using an entropy criterion, data points are evaluated with respect to the future trajectory of the closed loop system and are ``forgotten'' if the stability of the system can further be guaranteed. The approach is evaluated in a simulation and in a robotic experiment to show its real-time capability.