Bringing up robot: Fundamental mechanisms for creating a self-motivated, self-organizing architecture

In this paper we describe an intrinsic developmental algorithm designed to allow a mobile robot to incrementally progress through levels of increasingly-sophisticated behavior. We believe that the core ingredients for such a developmental algorithm are abstractions, anticipations, and self-motivations. We propose a multi-level, cascaded discovery and control architecture that includes the core ingredients. Toward this proposal we explore two novel models: a governor for automatically regulating the training of a neural network; and a path-planning neural network driven by patterns of “mental states” representing protogoals