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].

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.

This thesis presents a perceptual system for a humanoid robot that integrates abilities such as object localization and recognition with the deeper developmental machinery required to forge those competences out of raw physical experiences. It shows that a robotic platform can build up and maintain a system for object localization, segmentation, and recognition, starting from very little. What the robot starts with is a direct solution to achieving figure/ground separation: it simply `pokes around' in a region of visual ambiguity and watches what happens. If the arm passes through an area, that area is recognized as free space. If the arm collides with an object, causing it to move, the robot can use that motion to segment the object from the background. Once the robot can acquire reliable segmented views of objects, it learns from them, and from then on recognizes and segments those objects without further contact. Both low-level and high-level visual features can also be learned in this way, and examples are presented for both: orientation detection and affordance recognition, respectively.

Abstract- Social robots recognize and respond to human social cues with appropriate behaviors. The capabilities used to build social robots can be uniquely applied to assist in the diagnosis and treatment of autism, a pervasive developmental disorder which results in selective impairment of social abilities. This paper outlines some of the ways in which social robots can provide unique perspectives to address critical problems in diagnosing autism. We provide preliminary data and observations on how this result can be achieved based on three years of immersion in a clinical research group that performs diagnostic evaluations of more than 130 children per year.

Developmental robotics is an emerging field located at the intersection of developmental psychology and robotics, that has lately attracted quite some attention. This paper gives a survey of a variety of research projects dealing with or inspired by developmental issues, and outlines possible future directions.

Abstract – Effective communication between people and interactive robots will benefit if they have a common ground of understanding. I discuss how the common ground principle of least collective effort can be used to predict and design human robot interactions. Social cues lead people to create a mental model of a robot and estimates of its knowledge. People’s mental model and knowledge estimate will, in turn, influence the effort they expend to communicate with the robot. People will explain their message in less detail to a knowledgeable robot with which they have more common ground. This process can be leveraged to design interactions that have an appropriate style of robot direction and that accommodate to differences among people. Index Terms – human-robot interaction, social robots, humanoids, perception, dialogue, common ground, knowledge estimation, speech communication

Gaze following is the ability to re-direct one’s gaze to the location where another agent is looking. We present a computational model of how hu-man infants or other agents may acquire gaze following by learning to pre-dict the locations of interesting sights from the looking behavior of other agents through reinforcement learning. The model accounts for many find-ings about the development of gaze following in human infants. During learning, the model develops pre-motor representations that exhibit many properties characteristic of mirror neurons, but they are specific to looking behaviors. The existence of such a new class of mirror neurons is the main prediction of our model. The model also offers a parsimonious account of how these and possibly other mirror neurons may acquire their special re-sponse properties. In this account, visual representations of other agents’ actions become associated with pre-motor neurons that represent the inten-tion to perform corresponding actions. The model also demonstrates how this development may be obstructed in autism spectrum disorder, giving rise to specific physiological and anatomical differences in the mirror system.

Abstract — The ability to understand a teammate’s actions in terms of goals and other mental states is an important element of cooperative behavior. Simulation Theory argues in favor of an embodied approach whereby humans reuse parts of their cognitive structure for not only generating behavior, but also for simulating the mental states responsible for generating that behavior in others. We present our simulation-theoretic approach and demonstrates its performance in a collaborative task scenario. The robot offers its human teammate assistance by either inferring the human’s belief states to anticipate their informational needs, or inferring the human’s goal states to physically help the human achieve those goals.

Abstract. The paper surveys some of the mechanisms that have been demonstrated to be relevant for evolving communication systems in software simulations or robotic experiments. In each case, precursors or parallels with work in the study of artificial life and adaptive behaviour are discussed. 1

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