Investment Learning with Hierarchical PSOMs

Abstract

We propose a hierarchical scheme for rapid learning of context dependent skills that is based on the recently introduced Parameterized Self-Organizing Map (PSOM). The underlying idea is to first invest some learning effort to specialize the system into a rapid learner for a more restricted range of contexts. The specialization is carried out by a prior learning stage, during which the system acquires a set of basis mappings or skills for a set of prototypical contexts. Adaptation of a skill to a new context can then be achieved by interpolating in the space of the basis mappings and thus can be extremely rapid. We demonstrate the potential of this approach for the task of a 3D visuomotor map for a Puma robot and two cameras. This includes the forward and backward robot kinematics in 3D end effector coordinates, the 2D+2D retina coordinates and also the 6D joint angles. After the investment phase the transformation can be learned for a new camera set-up with a single observation.