Abstract

A critical challenge in robot learning from demonstration is the ability to map the behavior of the trainer onto a robot’s existing repertoire of basic/primitive capabilities. In part, this problem is due to the fact that the observed behavior of the teacher may consist of a combination of the robot’s individual primitives. The problem becomes more complex when the task involves temporal sequences of goals. We introduce an autonomous control architecture that allows for learning of hierarchical task representations, in which: every goal is achieved through a linear superposition of robot primitives and sequencing across goals is achieved through arbitration. We treat learning of the appropriate superposition as a state estimation problem over the space of possible linear fusion weights, inferred through a particle filter. We validate our approach in both simulated and real world environments with a Pioneer 3DX mobile robot.