Representation and constrained planning of manipulation strategies in the context of Programming by Demonstration

Abstract

In Programming by Demonstration, a flexible representation of manipulation motions is necessary to learn and generalize from human demonstrations. In contrast to subsymbolic representations of trajectories, e.g. based on a Gaussian Mixture Model, a partially symbolic representation of manipulation strategies based on a temporal satisfaction problem with domain constraints is developed. By using constrained motion planning and a geometric constraint representation, generalization to different robot systems and new environments is achieved. In order to plan learned manipulation strategies the RRT-based algorithm by Stilman et al. is extended to consider, that multiple sets of constraints are possible during the extension of the search tree.