This paper reviews "socially interactive robots": robots for which social human-robot interaction is important. We begin by discussing the context for socially interactive robots, emphasizing the relationship to other research fields and the di#erent forms of "social robots". We then present a taxonomy of design methods and system components used to build socially interactive robots. Finally, we describe the impact of these these robots on humans and discuss open issues. An expanded version of this paper, which contains a survey and taxonomy of current applications, is available as a technical report[61].

The paper proposes a number of models to examine through what mech-anisms a population of autonomous agents could arrive at a repertoire of perceptually grounded categories that is sufficiently shared to allow successful communication. The models are inspired by the main approaches to human categorisation being discussed in the literature: nativism, empiricism, and culturalism. Colour is taken as a case study. Although the paper takes no stance on which position is to be accepted as final truth with respect to hu-man categorisation and naming, it points to theoretical constraints that make each position more or less likely and contains clear suggestions on what the best engineering solution would be. Specifically, it argues that the collective choice of a shared repertoire must integrate multiple constraints, including constraints coming from communication.

This paper discusses the concept of joint attention and the di#erent skills underlying its development. We argue that joint attention is much more than gaze following or simultaneous looking because it implies a shared intentional relation to the world. The current state-of-the-art in robotic and computational models of the di#erent prerequisites of joint attention is discussed in relation with a developmental timeline drawn from results in child studies.

What kind of motivation drives child language development? This article presents a computational model and a robotic experiment to articulate the hypothesis that children discover communication as a result of exploring and playing with their environment. The considered robotic agent is intrinsically motivated towards situations in which it optimally progresses in learning. To experience optimal learning progress, it must avoid situations already familiar but also situations where nothing can be learnt. The robot is placed in an environment in which both communicating and non-communicating objects are present. As a consequence of its intrinsic motivation, the robot explores this environment in an organized manner focusing first on non-communicative activities and then discovering the learning potential of certain types of interactive behaviour. In this experiment, the agent ends up being interested by communication through vocal interactions without having a specific drive for communication.

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Properties of the physical world have shaped human evolutionary design and given rise to physically grounded mental representations. These grounded representations provide the foundation for higher level cognitive processes including language. Most natural language processing machines to date lack grounding. This paper advocates the creation of physically grounded language learning machines as a path toward scalable systems which can conceptualize and communicate about the world in human-like ways. As steps in this direction, two experimental language acquisition systems are presented.

Abstract. We present an approach for the supervised online learning of object representations based on a biologically motivated architecture of visual processing. We use the output of a recently developed topographical feature hierarchy to provide a view-based representation of threedimensional objects using a dynamical vector quantization approach. For a simple short-term object memory model we demonstrate real-time online learning of 50 complex-shaped objects within three hours. Additionally we propose some modifications of learning vector quantization algorithms that are especially adapted to the task of online learning and capable of effectively reducing the representational effort in a transfer from short-term to long-term memory. 1

Abstract. We present an artificial cognitive system for learning visual concepts. It comprises of vision, communication and manipulation subsystems, which provide visual input, enable verbal and non-verbal communication with a tutor and allow interaction with a given scene. The main goal is to learn associations between automatically extracted visual features and words that describe the scene in an open-ended, continuous manner. In particular, we address the problem of cross-modal learning of visual properties and spatial relations. We introduce and analyse several learning modes requiring different levels of tutor supervision. 1

In this paper we present the results of an experiment in which a collection of simulated robots that are evolved for the ability to solve a collective navigation problem develop a communication system that allows them to better cooperate. The analysis of the obtained results indicates how evolving robots develop a non-trivial communication system and exploit different communication modalities. Results also indicate how the possibility to co-adapt the robots ’ individual and social/communicative behaviour plays a key role in the development of progressively more complex and effective individuals. 1.