Technology is the future, woven into every aspect of our lives, but how are we to interact with all this technology and what happens when problems arise? Artificial agents, such as virtual characters and social robots could offer a realistic solution to help facilitate interactions between humans and machines-if only these agents were better equipped and more informed to hold up their end of an interaction. People and machines can interact to do things together, but in order to get the most out of every interaction, the agent must to be able to make reasonable judgements regarding your intent and goals for the interaction. We explore the concept of engagement from the different perspectives of the human and the agent. More specifically, we study how the agent perceives the engagement state of the other interactant, and how it generates its own representation of engaging behaviour. In this chapter, we discuss the different stages and components of engagement that have been suggested in the literature from the applied perspective of a case study of engagement for social robotics, as well as in the context of another study that was focused on gazerelated engagement with virtual characters.

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As increasingly more research efforts are geared towards creating robots that can teach and interact with children in educational contexts, it has been speculated that endowing robots with artificial empathy may facilitate learning. In this paper, we provide a background to the concept of empathy, and how it factors into learning. We then present our approach to equipping a robotic tutor with several empathic qualities, describing the technical architecture and its components, a map-reading learning scenario developed for an interactive multitouch table, as well as the pedagogical and empathic strategies devised for the robot. We also describe the results of a pilot study comparing the robotic tutor with these empathic qualities against a version of the tutor without them. The pilot study was performed with 26 school children aged 10–11 at their school. Results revealed that children in the test condition indeed rated the robot as more empathic than children in the control condition. Moreover, we explored several related measures, such as relational status and learning effect, yet no other significant differences were found. We further discuss these results and provide insights into future directions.

This paper investigates the effects of relative position and proxemics in the engagement process involved in Human-Robot collaboration. We evaluate the differences between two experimental placement conditions (frontal vs. lateral) for an autonomous robot in a collaborative task with a user across two different types of robot behaviours (helpful vs. neutral). The study evaluated placement and behaviour types around a touch table with 80 participants by measuring gaze, smiling behaviour, distance from the task, and finally electrodermal activity. Results suggest an overall user preference and higher engagement rates with the helpful robot in the frontal position. We discuss how behaviours and position of the robot relative to a user may affect user engagement and collaboration, in particular when the robot aims to provide help via socio-emotional bonding.

Engagement in task orientated social robotics is a complex phenomenon, consisting of both task and social elements. Previous work in this area tends to focus on these aspects in isolation without consideration for the positive or negative effects one might cause the other. We explore both, in an attempt to understand how engagement with the task might effect the social relationship with the robot, and vice versa. In this paper, we describe the analysis of participant self-report data collected during an exploratory pilot study used to evaluate users' "perception of engagement". We discuss how the results of our analysis suggest that ultimately, it was the users' own perception of the robots' characteristics such as friendliness, helpfulness and attentiveness which led to sustained engagement with both the task and robot.