Learning of generalized manipulation strategies in the context of Programming by Demonstration

Abstract

In Programming by Demonstration, abstract manipulation knowledge has to be learned, that can be used by an autonomous robot system in different environments with arbitrary obstacles. In this work, manipulation strategies are learned by observation of a human teacher and represented as a flexible, constraint-based representation of the search space for motion planning. The learned manipulation strategy contains a large set of automatically generated features, which are generalized using additional demonstrations of the teacher. The generalized manipulation strategy is executed on a real bimanual anthropomorphic robot system in different environments with arbitrary obstacles using constrained motion planning.