Social Robotics

The role of robots in society keeps expanding and diversifying, bringing with it a host of issues surrounding the relationship between robots and humans. This introduction to human-robot interaction (HRI), written by leading researchers in this developing field, is the first to provide a broad overview of the multidisciplinary topics central to modern HRI research. Students and researchers from robotics, artificial intelligence, psychology, sociology, and design will find it a concise and accessible guide to the current state of the field. Written for students from diverse backgrounds, it presents relevant background concepts, describing how robots work, how to design them, and how to evaluate their performance. Self-contained chapters discuss a wide range of topics, including the different communication modalities such as speech and language, non-verbal communication and the processing of emotions, as well as ethical issues around the application of robots today and in the context of our future society.

We present a large-scale study of a series of seven lessons designed to help young children learn English vocabulary as a foreign language using a social robot. The experiment was designed to investigate 1) the effectiveness of a social robot teaching children new words over the course of multiple interactions (supported by a tablet), 2) the added benefit of a robot’s iconic gestures on word learning and retention, and 3) the effect of learning from a robot tutor accompanied by a tablet versus learning from a tablet application alone. For reasons of transparency, the research questions, hypotheses and methods were preregistered. With a sample size of 194 children, our study was statistically well-powered. Our findings demonstrate that children are able to acquire and retain English vocabulary words taught by a robot tutor to a similar extent as when they are taught by a tablet application. In addition, we found no beneficial effect of a robot’s iconic gestures on learning gains.

Earlier research has shown that robots can provoke social responses in people, and that robots often elicit compliance. In this paper we discuss three proof of concept studies in which we explore the possibility of robots being hacked and taken over by others with the explicit purpose of using the robot’s social capabilities. Three scenarios are explored: gaining access to secured areas, extracting sensitive and personal information, and convincing people to take unsafe action. We find that people are willing to do these tasks, and that social robots tend to be trusted, even in situations that would normally cause suspicion.

Social robots can be used in education as tutors or peer learners. They have been shown to be effective at increasing cognitive and affective outcomes and have achieved outcomes similar to those of human tutoring on restricted tasks. This is largely because of their physical presence, which traditional learning technologies lack. We review the potential of social robots in education, discuss the technical challenges, and consider how the robot’s appearance and behavior affect learning outcomes.

Striking the right balance between robot autonomy and human control is a core challenge in social robotics, in both technical and ethical terms. On the one hand, extended robot autonomy offers the potential for increased human productivity and for the off-loading of physical and cognitive tasks. On the other hand, making the most of human technical and social expertise, as well as maintaining accountability, is highly desirable. This is particularly relevant in domains such as medical therapy and education, where social robots hold substantial promise, but where there is a high cost to poorly performing autonomous systems, compounded by ethical concerns. We present a field study in which we evaluate SPARC (supervised progressively autonomous robot competencies), an innovative approach addressing this challenge whereby a robot progressively learns appropriate autonomous behavior from in situ human demonstrations and guidance. Using online machine learning techniques, we demonstrate that the robot could effectively acquire legible and congruent social policies in a high-dimensional child-tutoring situation needing only a limited number of demonstrations while preserving human supervision whenever desirable. By exploiting human expertise, our technique enables rapid learning of autonomous social and domain-specific policies in complex and nondeterministic environments. Last, we underline the generic properties of SPARC and discuss how this paradigm is relevant to a broad range of difficult human-robot interaction scenarios.