User-guided reinforcement learning of robot assistive tasks for an intelligent environment

Autonomous robots hold the possibility of performing a variety of assistive tasks in intelligent environments. However, widespread use of robot assistants in these environments requires ease of use by individuals who are generally not skilled robot operators. In this paper we present a method of training robots that bridges the gap between user programming of a robot and autonomous learning of a robot task. With our approach to variable autonomy, we integrate user commands at varying levels of abstraction into a reinforcement learner to permit faster policy acquisition. We illustrate the ideas using a robot assistant task, that of retrieving medicine for an inhabitant of a smart home.