Robot learning by demonstration using forward models of schema-based behaviors

A significant challenge in designing robot systems that learn from a teacher’s demonstration is the ability to map the perceived behavior of the trainer to an existing set of primitive behaviors. A main difficulty is that the observed actions may constitute a combination of individual behaviors’ outcomes, which would require a decomposition of the observation onto multiple primitive behaviors. This paper presents an approach to robot learning by demonstration that uses a potential-field behavioral representation to learn tasks composed by superposition of behaviors. The method allows a robot to infer essential aspects of the demonstrated tasks, which could not be captured if combinations of behaviors would not have been considered. We validate our approach in a simulated environment with a Pioneer 3DX mobile robot.