Teaching a virtual robot to perform tasks by learning from observation

Abstract

We propose a methodology based on Learning from Observation in order to teach a virtual robot to perform its tasks. Our technique only assumes that behaviors to be cloned can be observed and represented using a finite alphabet of symbols. A virtual agent is used to generate training material, according to a range of strategies of gradually increasing complexity. We use Machine Learning techniques to learn new strategies by observing and thereafter imitating the actions performed by the agent. We perform several experiments to test our proposal. The analysis of those experiments suggests that probabilistic finite state machines could be a suitable tool for the problem of behavioral cloning. We believe that the given methodology is easy to integrate in the learning module of any Ubiquitous Robot Architecture.