Experience-Based Model Selection to Enable Long-Term, Safe Control for Repetitive Tasks Under Changing Conditions

Abstract

Learning approaches have enabled significant performance improvements in robotic control allowing robots to execute motions that were previously impossible. The majority of the work to date, however, assumes that the parts to be learned are static or slowly changing, which limits their applicability in realistic scenarios with rapid changes in the conditions. This paper presents a method to extend an existing single-mode safe learning controller based on Gaussian Process Regression to learn an increasing number of non-linear models for the robot dynamics. We show that this approach enables a robot to re-use past experiences from a large number of previously visited operating conditions, and to safely adapt when a new and distinct operating condition is encountered. This allows the robot to achieve safety and high performance in a large number of operating conditions that do not have to be specified ahead of time. Our approach runs independently from the controller, imposing no additional computation time on the control loop regardless of the number of previous operating conditions considered. We demonstrate the effectiveness of our approach in experiment on a 900 kg ground robot with both physical and artificial changes to its dynamics. All of our experiments are conducted using vision for localization.