Designing robots for entertainment scenarios presents many of the same challenges encountered in other Human-Robot Interaction (HRI) deployments, such as maintaining relationships over time, operating in unpredictable and noisy environments, and personalizing interactions for diverse users and user groups. However, the success of such interactions also hinges on additional factors such as personality, expressivity, and storytelling. Whether creating a new robotic character, or bringing an existing character to life, a great deal of both technical and artistic collaboration is required to create compelling experiences. In this talk, I will showcase several robotic character projects that are at various stages of real-world deployment and testing. Using these projects as examples, I will discuss the process of going from a research prototype to creating believable, expressive and engaging characters. This will include dynamic robots that are designed for large audiences, where nonverbal behavior is crucial, and robots that interact with smaller groups, relying more heavily on conversation and language understanding.

This paper presents an experiment with three artificial agents adopting different strategies when being interrupted by human conversational partners. The agent either ignored the interruption (the most common behavior in conversational engines to date), yielded the turn to the human conversational partner right away, or acknowledged the interruption, finished its thought and then responded to the content of the interruption. Our results show that this change in the agent’s conversational behavior had a significant impact on which personality traits people assigned to the agent, as well as how much they enjoyed interacting with it. Moreover, the data also indicates that human interlocutors adapted their own conversational behavior. Our findings suggest that the interactive behavior of an artificial agent should be carefully designed to match its desired personality and the intended conversational dynamics.

We propose a method for detecting the group’s focus of attention: the visual point at which a majority of participants direct their gaze in a conversation. This information enables a robot to infer important conversational cues and adjust its behavior to support more natural conversational interactions. Our approach uses a Hidden Markov Model based on mimicry, where the robot observes the head orientation of participants and infers their gaze direction to identify the group’s focus of attention. We demonstrate our method by replicating the gaze patterns of the group members, showing that the robot can accurately determine the focal point. We evaluated our algorithm using a combination of datasets and real-world scenarios with a Fetch robot, demonstrating an accuracy of 81% compared to a baseline of 54%. Our proposed method has the potential to significantly improve group-oriented human-robot interaction.

In order to achieve more believable interactions with artificial agents, there is a need to produce dialogue that is not only relevant, but also emotionally appropriate and consistent. This paper presents a comprehensive system that models the emotional state of users and an agent to dynamically adapt dialogue utterance selection. A Partially Observable Markov Decision Process (POMDP) with an online solver is used to model user reactions in real-time. The model decides the emotional content of the next utterance based on the rewards from the users and the agent. The previous approaches are extended through jointly modeling the user and agent emotions, maintaining this model over time with a memory, and enabling interactions with multiple users. A proof of concept user study is used to demonstrate that the system can deliver and maintain distinct agent personalities during multiparty interactions.

Smiles and laughs have been the subject of many studies over the past decades, due to their frequent occurrence in interactions, as well as their social and emotional functions in dyadic conversations. In this paper we push forward previous work by providing a first study on the influence one interacting partner’s smiles and laughs have on their interlocutor’s, taking into account these expressions’ intensities. Our second contribution is a study on the patterns of laugh and smile sequences during the dialogs, again taking the intensity into account. Finally, we discuss the effect of the interlocutor’s role on smiling and laughing. In order to achieve this, we use a database of naturalistic dyadic conversations which was collected and annotated for the purpose of this study. The details of the collection and annotation are also reported here to enable reproduction.

Robotics is a multidisciplinary and highly innovative field. Recently, multiple and often minimally connected sub-communities of child-robot interaction have started to emerge, variously focusing on the design issues, engineering, and applications of robotic platforms and toolkits. Despite increasing public interest in robots, including robots for children, child-robot interaction research remains highly fragmented and lacks regular cross-disciplinary venues for discussion and dissemination. This workshop will bring together researchers with diverse scientific backgrounds. It will serve as a venue in which to reflect on the current circumstances in which child-robot research is conducted, articulate emerging and "near future" challenges, and discuss actions and tools with which to meet those challenges and consolidate the field.

To feel novel and engaging over time it is critical for an autonomous agent to have a large corpus of potential responses. As the size and multi-domain nature of the corpus grows, however, traditional hand-authoring of dialogue content is no longer practical. While crowdsourcing can help to overcome the problem of scale, a diverse set of authors contributing independently to an agent's language can also introduce inconsistencies in expressed behavior. In terms of affect or mood, for example, incremental authoring can result in an agent who reacts calmly at one moment but impatiently moments later with no clear reason for the transition. In contrast, affect in natural conversation develops over time based on both the agent's personality and contextual triggers. To better achieve this dynamic, an autonomous agent needs to (a) have content and behavior available for different desired affective states and (b) be able to predict what affective state will be perceived by a person for a given behavior. In this proof-of-concept paper, we explore a way to elicit and evaluate affective behavior using crowdsourcing. We show that untrained crowd workers are able to author content for a broad variety of target affect states when given semi-situated narratives as prompts. We also demonstrate that it is possible to strategically combine multimodal affective behavior and voice content from the authored pieces using a predictive model of how the expressed behavior will be perceived.

As social robots increasingly enter people's lives, coherence of personality is an important challenge for longterm human-robot interactions. We extend an architecture that acquires dialog through crowdsourcing to author both verbal and non-verbal indicators of personality. We demonstrate the efficacy of the approach through a four-day study in which teams of participants interacted with a social robot expressing one of two personalities as the host of a competitive game. Results indicate that the system is able to elicit personality-driven language behaviors from the crowd in an incremental and ongoing way and produce a coherent expression of that personality during face-to-face interactions over time.

An increasing number of human-robot interaction (HRI) studies are now taking place in applied settings with children. These interactions often hinge on verbal interaction to effectively achieve their goals. Great advances have been made in adult speech recognition and it is often assumed that these advances will carry over to the HRI domain and to interactions with children. In this paper, we evaluate a number of automatic speech recognition (ASR) engines under a variety of conditions, inspired by real-world social HRI conditions. Using the data collected we demonstrate that there is still much work to be done in ASR for child speech, with interactions relying solely on this modality still out of reach. However, we also make recommendations for child-robot interaction design in order to maximise the capability that does currently exist.

Human-Robot Interaction (HRI) research requires the integration and cooperation of multiple disciplines, technical and social, in order to make progress. In many cases using different motivations, each of these disciplines bring with them different assumptions and methodologies. We assess recent trends in the field of HRI by examining publications in the HRI conference over the past three years (over 100 full papers), and characterise them according to 14 categories. We focus primarily on aspects of methodology. From this, a series of practical recommendations based on rigorous guidelines from other research fields that have not yet become common practice in HRI are proposed. Furthermore, we explore the primary implications of the observed recent trends for the field more generally, in terms of both methodology and research directions. We propose that the interdisciplinary nature of HRI must be maintained, but that a common methodological approach provides a much needed frame of reference to facilitate rigorous future progress.

An increasing amount of research is being conducted to determine how a robot tutor should behave socially in educational interactions with children. Both human-human and human-robot interaction literature predicts an increase in learning with increased social availability of a tutor, where social availability has verbal and nonverbal components. Prior work has shown that greater availability in the nonverbal behaviour of a robot tutor has a positive impact on child learning. This paper presents a study with 67 children to explore how social aspects of a tutor robot's speech influences their perception of the robot and their language learning in an interaction. Children perceive the difference in social behaviour between `low' and `high' verbal availability conditions, and improve significantly between a pre- and a post-test in both conditions. A longer-term retention test taken the following week showed that the children had retained almost all of the information they had learnt. However, learning was not affected by which of the robot behaviours they had been exposed to. It is suggested that in this short-term interaction context, additional effort in developing social aspects of a robot's verbal behaviour may not return the desired positive impact on learning gains.

The field of Human-Robot Interaction (HRI) is increasingly exploring the use of social robots for educating children. Commonly, non-academic audiences will ask how robots compare to humans in terms of learning outcomes. This question is also interesting for social roboticists as humans are often assumed to be an upper benchmark for social behaviour, which influences learning. This paper presents a study in which learning gains of children are compared when taught the same mathematics material by a robot tutor and a non-expert human tutor. Significant learning occurs in both conditions, but the children improve more with the human tutor. This difference is not statistically significant, but the effect sizes fall in line with findings from other literature showing that humans outperform technology for tutoring. We discuss these findings in the context of applying social robots in child education.

Subjective appreciation and performance evaluation of a robot by users are two important dimensions for Human-Robot Interaction, especially as increasing numbers of people become involved with robots. As roboticists we have to carefully design robots to make the interaction as smooth and enjoyable as possible for the users, while maintaining good performance in the task assigned to the robot. In this paper, we examine the impact of providing a robot with learning capabilities on how users report the quality of the interaction in relation to objective performance. We show that humans tend to prefer interacting with a learning robot and will rate its capabilities higher even if the actual performance in the task was lower. We suggest that adding learning to a robot could reduce the apparent load felt by a user for a new task and improve the user's evaluation of the system, thus facilitating the integration of such robots into existing work flows.

The role of robot social behaviour in changing people's behaviour is an interesting and yet still open question, with the general assumption that social behaviour is beneficial. In this study, we examine the effect of socially contingent robot behaviours on a charity collection task. Manipulating only behavioural cues (maintaining the same verbal content), we show that when the robot exhibits contingent behaviours consistent with those observable in humans, this results in a 32% increase in money collected over a non-reactive robot. These results suggest that apparent social agency on the part of the robot, even when subtle behavioural cues are used, can result in behavioural change on the part of the interacting human.

Social robots are finding increasing application in the domain of education, particularly for children, to support and augment learning opportunities. With an implicit assumption that social and adaptive behaviour is desirable, it is therefore of interest to determine precisely how these aspects of behaviour may be exploited in robots to support children in their learning. In this paper, we explore this issue by evaluating the effect of a social robot tutoring strategy with children learning about prime numbers. It is shown that the tutoring strategy itself leads to improvement, but that the presence of a robot employing this strategy amplifies this effect, resulting in significant learning. However, it was also found that children interacting with a robot using social and adaptive behaviours in addition to the teaching strategy did not learn a significant amount. These results indicate that while the presence of a physical robot leads to improved learning, caution is required when applying social behaviour to a robot in a tutoring context.

In this contribution, we describe a method of analysing and interpreting the direction and timing of a human's gaze over time towards a robot whilst interacting. Based on annotated video recordings of the interactions, this post-hoc analysis can be used to determine how this gaze behaviour changes over the course of an interaction, following from the observation that humans change their behaviour towards the robot on the time-scale of individual interactions. We posit that given these circumstances, this measure may be used as a proxy (among others) for engagement in the interaction or the human's attribution of social agency to the robot. Application of this method to a sample of unstructured child-robot interactions demonstrates its use, and justifies its utilisation in future studies.

Compliance studies in human-robot interaction (HRI) tend to consist of direct requests from the robot to the human. It is suggested that indirect requests are considered more polite, which has been positively correlated with learning gains. An experiment is conducted to explore compliance with indirect robot requests in teaching interactions. A comparison is made across embodiment conditions, but no significant differences are found. Overall, children comply with the robot's requests, which is used to support the hypothesis that given a well-defined context, children will infer the indirect meaning of a suggestion from a robot.

No abstract available.

When studying social interactions, robust data collection protocols can come at the expense of allowing a natural interaction to take place because of a rigid structure in the experimental scenario. This work seeks to explore the use of an interaction mediator as a tool to constrain the content of a social interaction without imposing an interaction structure. Two studies were conducted in different interaction contexts using children: a peer-peer interaction, and a teacher-child interaction. Given that no interaction structure is imposed, objective metrics to characterise behaviour prove difficult to apply. Qualitative analysis techniques, namely Conversation Analysis, are therefore used to study the dyadic interactions. Results confirm the role of the mediating device in providing interaction content without imposing interaction structure. This illustrates the potential role of such devices in manipulating social interactions to facilitate empirical interrogation.