72 4 Brief Survey of Cognitive Architectures
as to include an implicit “cup versus bow!” 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 [IIZT* 02] and SATL [WIZ* 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 underlying philosophy is based on human child development [WT106], the knowledge representations involved are neural network based, and a number of novel learning algorithms are involved, especially in the area of vision processing.
e FLOWERS [BO09], an initiative at the French research institute INRIA, led by Pierre- Yves Oudeyer, is also based on a principle of trying to reconstruct the processes of devel- opment of the human child’s mind, spontaneously driven by intrinsic motivations. Kaplan [Kap08] has taken this project in a direction closely related to our own via the creation of a “robot playroom.” Experiential language learning has also been a focus of the project [OK06], driven by innovations in speech understanding.
e IM-CLEVER|, a new European project coordinated by Gianluca Baldassarre and con- ducted by a large team of researchers at different institutions, is focused on creating software enabling an iCub [MSV ~ 08] humanoid robot to explore the environment and learn to carry out human childlike behaviors based on its own intrinsic motivations. As this project is the closest to our own we will discuss it in more depth below.
Like CogPrime, IM-CLEVER is a humanoid robot intelligence architecture guided by intrin- sic motivations, and using hierarchical architectures for reinforcement learning and sensory ab-
lhttp://im-clever.noze.it/project /project-description
HOUSE_OVERSIGHT_012988