Learning Probabilistic Models for Safe Predictive Control in Unknown Environments

Christopher D. McKinnon, Angela P. Schoellig

Abstract: Researchers rely increasingly on tools from machine learning to improve the performance of control algorithms on real world tasks and enable robots to operate for long periods of time without intervention. Many of these algorithms require a model for the dynamics of the robot. In particular, researchers designing methods for safe learning control often rely on an upper bound on model error to make guarantees about the worst-case closed-loop performance of their algorithm. There are different options for how to learn such a model of the robot dynamics. We study probabilistic models for use in the context of stochastic model predictive control. Two popular choices for learning the robot dynamics are Gaussian Process (GP) regression and various forms of local linear regression. In this paper, we present a study comparing GPs with a particular form of local linear regression for learning robot dynamics with the aim of guaranteeing safety when a robot operates in novel conditions. We show results based on experimental data from a 900 kg ground robot using vision for localisation.