Visuospatial skill learning for object reconfiguration tasks

Abstract

We present a novel robot learning approach based on visual perception that allows a robot to acquire new skills by observing a demonstration from a tutor. Unlike most existing learning from demonstration approaches, where the focus is placed on the trajectories, in our approach the focus is on achieving a desired goal configuration of objects relative to one another. Our approach is based on visual perception which captures the object's context for each demonstrated action. This context is the basis of the visuospatial representation and encodes implicitly the relative positioning of the object with respect to multiple other objects simultaneously. The proposed approach is capable of learning and generalizing multi-operation skills from a single demonstration, while requiring minimum a priori knowledge about the environment. The learned skills comprise a sequence of operations that aim to achieve the desired goal configuration using the given objects. We illustrate the capabilities of our approach using three object reconfiguration tasks with a Barrett WAM robot.