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].

Human-agent interaction is a growing area of research; there are many approaches that address significantly different aspects of agent social intelligence. In this paper, we focus on a robotic domain in which a human acts both as a teacher and a collaborator to a mobile robot. First, we present an approach that allows a robot to learn task representations from its own experiences of interacting with a human. While most approaches to learning from demonstration have focused on acquiring policies (i.e., collections of reactive rules), we demonstrate a mechanism that constructs high-level task representations based on the robot's underlying capabilities. Second, we describe a generalization of the framework to allow a robot to interact with humans in order to handle unexpected situations that can occur in its task execution. Without using explicit communication, the robot is able to engage a human to aid it during certain parts of task execution. We demonstrate our concepts with a mobile robot learning various tasks from a human, and, when needed, interacting with a human to get help performing them.

This chapter addresses embodied social interaction inlif6 like agents. Embodiment is discussedf rom both arti cial intelligence and psychology viewpoints. Di#erent degreesof embodiment in biological, virtual and robotic agents are discussed, given the example of a bottomup, behavior-oriented, dynamic control of virtual robots. A `dancing with strangers' experiment shows how the same principles can be applied to physical robot-human interaction. We then discuss the issue of sociality which di#ers in di#erent academic communities with respect to which roles are attributed to genes, memes, and the individual embodied agent.

INTRODUCTION: THE LIFE-LIKE AGENT HYPOTHESIS This paper discusses the role of predictability and control in robot-human interaction. This involves the central question whether humans are good models for synthetic (social) agents. Design issues based on cognitive accounts towards robot-human interaction are discussed with respect to the author’s recent work on building interactive robotic systems as remedial tools

In this paper socially intelligent agents (SIA) are understood as agents which do not only from an observer point of view behave socially but which are able to recognize and identify other agents and establish and maintain relationships to other agents. The process of building socially intelligent agents is influenced by what the human as the designer considers `social', and conversely agent tools which are behaving socially can influence human conceptions of sociality. A Cognitive Technology (CT) approach towards designing SIA affords as an opportunity to study the process of 1) how new forms of interactions and functionalities and use of technology can emerge at the human-tool interface, 2) how social agents can constrain their cognitive and social potential, and 3) how social agent technology and human (social) cognition can co-evolve and co-adapt and result in new forms of sociality. Agent-human interaction requires a cognitive fit between SIA technology and the human-in-the-loop a...

This paper addresses the role of imitation as a means to enhance the learning of communication skills in autonomous robots. A series of robotic experiments are presented in which autonomous mobile robots are taught a synthetic proto-language. Learning of the language occurs through an imitative scenario where the robot replicates the teacher's movements. Imitation is here an implicit attentional mechanism which allows the robot imitator to share a similar set of proprio- and exteroceptions with the teacher. The robot grounds its understanding of the teacher's words, which describe the teacher's current observations, upon its own perceptions which are similar to those of the teacher. Learning of the robot is based on a dynamical recurrent associative memory architecture (DRAMA). Learning is unsupervised and results from the self-organization of the robot's connectionist architecture. Results show that the imitative behavior greatly improves the efficiency and speed of the learning. More...

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

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