Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.

What is the enactive approach to cognition? Over the last 15 years this banner has grown to become a respectable alternative to traditional frameworks in cognitive science. It is at the same time a label with different interpretations and upon which different doubts have been cast. This paper elaborates on the core ideas that define the enactive approach and their implications: autonomy, sensemaking, emergence, embodiment, and experience. These are coherent, radical and very powerful concepts that establish clear methodological guidelines for research. The paper also looks at the problems that arise from taking these ideas seriously. The enactive approach has plenty of room for elaboration in many different areas and many challenges to respond to. In particular, we concentrate on the problems surrounding several theories of value-appraisal and valuegeneration. The enactive view takes the task of understanding meaning and value very seriously and elaborates a proper scientific alternative to reductionist attempts to tackle these issues by functional localization. Another area where the enactive framework can make a significant contribution is social interaction and

Cleeremans: The search for the computational correlates of consciousness

Abstract. This paper, combining the standpoints of philosophy and Artificial Intelligence with theoretical psychology, summarises several decades of investigation by the author of the variety of functions of vision in humans and other animals, pointing out that biological evolution has solved many more problems than are normally noticed. For example, the biological functions of human and animal vision are closely related to the ability of humans to do mathematics, including discovering and proving theorems in geometry, topology and arithmetic. Many of the phenomena discovered by psychologists and neuroscientists require sophisticated controlled laboratory settings and specialised measuring equipment, whereas the functions of vision reported here mostly require only careful attention to a wide range of everyday competences that easily go unnoticed. Currently available computer models and neural theories are very far from explaining those functions, so progress in explaining how vision works is more in need of new proposals for explanatory mechanisms than new laboratory data. Systematically formulating the requirements for such mechanisms is not easy. If we start by analysing familiar competences, that can suggest new experiments to clarify precise forms of these competences, how they develop within individuals, which other species have them, and how performance varies according to conditions. This will help to constrain requirements for models purporting to explain how the competences work. For example, Gibson’s theory of affordances needs a number of extensions, including allowing affordances to be composed in several ways from lower level proto-affordances. The paper ends with speculations regarding the need for new kinds of information-processing machinery to account for the phenomena.

Some issues concerning requirements for architectures, mechanisms, ontologies and forms of representation in intelligent human-like or animal-like robots are discussed. The tautology that a robot that acts and perceives in the world must be embodied is often combined with false premises, such as the premiss that a particular type of body is a requirement for intelligence, or for human intelligence, or the premiss that all cognition is concerned with sensorimotor interactions, or the premiss that all cognition is implemented in dynamical systems closely coupled with sensors and effectors. It is time to step back and ask what robotic research in the past decade has been ignoring. I shall try to identify some major research gaps by a combination of assembling requirements that have been largely ignored and design ideas that have not been investigated – partly because at present it is too difficult to make significant progress on those problems with physical robots, as too many different problems need to be solved simultaneously. In particular, the importance of studying some abstract features of the environment about which the animal or robot has to learn (extending ideas of J.J.Gibson) has not been widely appreciated. 1

Abstract. A child, or young human-like robot of the future, needs to develop an information-processing architecture, forms of representation, and mechanisms to support perceiving, manipulating, and thinking about the world, especially perceiving and thinking about actual and possible structures and processes in a 3-D environment. The mechanisms for extending those representations and mechanisms, are also the core mechanisms required for developing mathematical competences, especially geometric and topological reasoning competences. Understanding both the natural processes and the requirements for future human-like robots requires AI designers to develop new forms of representation and mechanisms for geometric and topological reasoning to explain a child’s (or robot’s) development of understanding of affordances, and the proto-affordances that underlie them. A suitable multi-functional self-extending architecture will enable those competences to be developed. Within such a machine, human-like mathematical learning will be possible. It is argued that this can support Kant’s philosophy of mathematics, as against Humean philosophies. It also exposes serious limitations in studies of mathematical development by psychologists. Keywords: learning mathematics, philosophy of mathematics, robot 3-D vision, self-extending architecture, epigenetic robotics 1 Introduction: Approaches

This paper discusses some of the long term objectives of cognitive robotics and some of the requirements for meeting those objectives that are still a very long way off. These include requirements for visual perception, for architectures, for kinds of learning, and for innate competences needed to drive learning and development in a variety of different environments. The work arises mainly out of research on requirements for forms of representation and architectures within the PlayMate scenario, which is a scenario concerned with a robot that perceives, interacts with and talks about 3-D objects on a tabletop, one of the scenarios in the EC-funded CoSy Robotics project. Long term goals Researchers working in cognitive robotics do not all have the

Abstract. We present a schema-based agent architecture which is inspired by an ethological model of the praying mantis. It includes an inner state, perceptual and motor schemas, several routines, a fovea and a motor. We describe the design and implementation of the architecture and we use it for comparing two models: the former uses reactive, stimulusresponse schemas; the latter involves also forward models, which are used by the schemas for generating predictions. Our results show an advantage in using anticipatory components inside the schemas 1. 1

The current essay introduces the guidance theory of representation, according to which the content and intentionality of representations can be accounted for in terms of the way they provide guidance for action. The guidance theory offers a way of fixing representational content that gives the causal and evolutionary history of the subject only an indirect (non-necessary) role, and an account of representational error, based on failure of action, that does not rely on any such notions as proper functions, ideal conditions, or normal circumstances. Moreover, because the notion of error is defined in terms of failure of action, the guidance theory meets the “meta-epistemological requirement” that representational error should be potentially detectable by the representing system itself. In this essay, we offer a brief account of the biological origins of representation, a formal characterization of the guidance theory, some examples of its use, and show how the guidance theory handles some traditional problem cases for representation: the representation of fictional and abstract entities. Being both representational and actiongrounded, the guidance theory may provide some common ground between embodied and cognitivist approaches to the study of the mind.

We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for behavior planning. State representations are learned with a growing selforganizing layer which is directly coupled to a perception and a motor layer. Knowledge about possible state transitions is encoded in the lateral connectivity. Motor signals modulate this lateral connectivity and a dynamic field on the layer organizes a planning process. All mechanisms are local and adaptation is based on Hebbian ideas. The model is continuous in the action, perception, and time domain.