This paper explores the topic of social robots -- the class of robots that people anthropomorphize in order to interact with them. From the diverse and growing number of applications for such robots, a few distinct modes of interaction are beginning to emerge. We distinguish four such classes: socially evocative, socially communicative, socially responsive, and sociable. For the remainder of the paper, we explore a few key features of sociable robots that distinguishes them from the others. We use the vocal turn-taking behavior of our robot, Kismet, as a case study to highlight these points.

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].

We want to build robots capable of rich social interactions with humans, including natural communication and cooperation. This work explores how imitation as a social learning and teaching process may be applied to building socially intelligent robots, and summarizes our progress toward building a robot capable of learning how to imitate facial expressions from simple imitative games played with a human, using biologically inspired mechanisms. Our approach is heavily influenced by the ways human infants learn to communicate with their caregivers and understand the actions of others in intentional terms. Among the key ideas that we draw from work on the development of human social intelligence, the most crucial is the hypothesis that in human infants, imitative interactions, starting with facial mimicry, are a significant stepping-stone in developing appropriate social behavior, learning to predict other’s actions, and ultimately, understanding the intensions of others. 1

Abstract — The purpose of our research is to develop a humanoid museum guide robot that performs intuitive, multimodal interaction with multiple persons. In this paper, we present a robotic system that makes use of visual perception, sound source localization, and speech recognition to detect, track, and involve multiple persons into interaction. Depending on the audio-visual input, our robot shifts its attention between different persons. In order to direct the attention of its communication partners towards exhibits, our robot performs gestures with its eyes and arms. As we demonstrate in practical experiments, our robot is able to interact with multiple persons in a multimodal way and to shift its attention between different people. Furthermore, we discuss experiences made during a two-day public demonstration of our robot. I.

Abstract — An essential capability for a robot designed to interact with humans is to show attention to the people in its surroundings. To enable a robot to involve multiple persons into interaction requires the maintenance of an accurate belief about the people in the environment. In this paper, we use a probabilistic technique to update the knowledge of the robot based on sensory input. In this way, the robot is able to reason about the uncertainty in its belief about people in the vicinity and is able to shift its attention between different persons. Even people who are not the primary conversational partners are included into the interaction. In practical experiments with a humanoid robot, we demonstrate the effectiveness of our approach. I.

Attracting people and involving multiple persons into an interaction is an essential capability for a humanoid robot. A prerequisite for such a behavior is that the robot is able to sense people in its vicinity and to know where they are located. In this paper, we propose an approach that maintains a probabilistic belief about people in the surroundings of the robot. Using this belief, the robot is able to memorize people even if they are currently outside its limited field of view. Furthermore, we use a technique to localize a speaker in the environment. In this way, even people who are currently not the primary conversational partners or who are not stored in the robot’s belief can attract its attention. To enrich human-robot interaction and to express how the robot changes its mood, we apply a technique to change its facial expressions. As we demonstrate in practical experiments, by integrating the presented techniques into its control architecture, our robot is able to interact with multiple persons in a multimodal way and to shift its attention between different people.

To a robot, the world is a sea of ambiguity, in which it will sink or swim depending on the robustness of its perceptual abilities. But robust machine perception has proven difficult to achieve. This paper argues that robots must be given not just particular perceptual competences, but the tools to forge those competences out of raw physical experiences.

Showing attentiveness to people is an essential capability for a robot designed to interact with humans. Involving several persons into an interaction requires that the robot knows where the persons are relatively to its current location. Therefore, we propose an approach that maintains a probabilistic belief about people in the surroundings of the robot which is updated based on sensory information. Using this belief the robot is able to memorize people even if they are currently outside its limited field of view. Furthermore, we use a technique to localize a speaker in the environment. In this way, even people who are currently not stored in the belief of the robot can attract its attention. As we show in practical experiments, our humanoid robot is able to shift its attention between different persons. Even people who are not the primary conversational partner are included into the interaction. 1