Increasing the Autonomy of Mobile Robots by On-line Learning Simultaneously at Different Levels of Abstraction

Abstract

We present a framework that is able to handle system and environmental changes by learning autonomously at different levels of abstraction. It is able to do so in continuous and noisy environments by 1) an active strategy learning module that uses reinforcement learning and 2) a dynamically adapting skill module that proactively explores the robot's own action capabilities and thereby providing actions to the strategy module. We present results that show the feasibility of simultaneously learning low-level skills and high-level strategies in order to reach a goal while reacting to disturbances like hardware damages. Thereby, the robot drastically increases its overall autonomy.