72 4 Brief Survey of Cognitive Architectures as to include an implicit “cup versus bowl” classifier, whose inputs are the outputs of some of the nodes in the higher levels of the perceptual network. This classifier belongs in the action network because it is part of the procedure by which the DeSTIN system carries out the action of identifying an object as a cup or a bowl. This example illustrates how the learning of complex concepts and procedures is divided fluidly between the perceptual network, which builds a model of the world in an unsupervised way, and the action network, which learns how to respond to the world in a manner that will receive positive reinforcement from the critic network. 4.3.2 Developmental Robotics Architectures A particular subset of emergentist cognitive architectures are sufficiently important that we consider them separately here: these are developmental robotics architectures, focused on con- trolling robots without significant “hard-wiring” of knowledge or capabilities, allowing robots to learn (and learn how to learn, etc.) via their engagement with the world. A significant focus is often placed here on “intrinsic motivation,” wherein the robot explores the world guided by internal goals like novelty or curiosity, forming a model of the world as it goes along, based on the modeling requirements implied by its goals. Many of the foundations of this research area were laid by Juergen Schmidhuber’s work in the 1990s [Sch91b, Sch9la, Sch95, Sch02], but now with more powerful computers and robots the area is leading to more impressive practical demonstrations. We mention here a handful of the important initiatives in this area: e Juyang Weng’s Dav [I1ZT* 02] and SAIL [WIHZ‘ 00] projects involve mobile robots that explore their environments autonomously, and learn to carry out simple tasks by building up their own world-representations through both unsupervised and teacher-driven processing of high-dimensional sensorimotor data. The u