In teams of humans and several robots, communication within the robot sub-group may occur without the human being necessarily aware of this, e.g., to update each other about task-relevant aspects of the environment. Nonetheless, since this affects their subsequent actions and visible behaviour, it has consequences for teamwork and the humans’ perception of the team, which are not always well-understood. Intra-robot communication and coordination can be beneficial, but may also be experienced negatively due to unexpected group dynamics.In this study, we designed three robot teams, with varying levels of shared learning: one where robots share information about the environment and other robots acknowledge receiving this information, one where this information is shared but receipt is not acknowledged, and one where there is no communication. In each case, robots assisted a human participant in repairing pipes in a simulated environment. We measured perceived entitativity, trust, and attribution of mind to the robots.Overall, our results illustrate that improving the skills of robots (in this case, shared learning) is not sufficient to also improve the human experience of being a member of such a team. How humans perceive artificial agents can be more important than their actual abilities. We thus explore implications for improving the human teammate’s understanding of robotic (social) abilities in hybrid teams.

This work presents a framework for a visual-auditory attention-driven robot eye-head gaze movement, which combines visual and auditory inputs to determine the direction of gaze movement for a social robot. The framework computes the most salient changes in position by considering both visual and auditory cues. The proposed system was implemented on Haru, a tabletop social robot, where eye-head gaze movement was controlled using visual input from a camera positioned above the eyes and auditory input from a seven-channel microphone. This allowed for eye movement on a two-dimensional flat screen and body rotation towards the person who is speaking. This framework provides a representation of the robot’s attentional gaze that leverages both visual and auditory cues, resulting in more natural and responsive coordinated eye-head gaze movements of the social robot. The potential benefits include improved communication, increased engagement, and a stronger sense of connection with the robot.

The termmultimodalityhas come to take on several somewhat different meanings depending on the underlying theoretical paradigms and traditions along with the purpose and context of use. The term is closely related toembodiment, which, in turn, is also used in several different ways. In this article, we elaborate on this connection and propose that a pragmatic and pluralistic stance is appropriate for multimodality. We further propose a distinction between first- and second-order effects of multimodality—what is achieved by multiple modalities in isolation and the opportunities that emerge when several modalities are entangled. This highlights questions regarding ways to cluster or interchange different modalities, for example, through redundancy or degeneracy. Apart from discussing multimodality with respect to an individual agent, we further look to more distributed agents and situations in which social aspects become relevant. In robotics, understanding the various uses and interpretations of these terms can prevent miscommunication when designing robots as well as increase awareness of the underlying theoretical concepts. Given the complexity of the different ways in which multimodality is relevant in social robotics, this can provide the basis for negotiating appropriate meanings of the term on a case-by-case basis.

This interdisciplinary workshop aims to break boundaries between the researchers who develop human models (e.g., from the fields of human factors, cognitive psychology, and computational neuroscience) and roboticists who use human models in different human-robot interaction (HRI) contexts. The keynote talks, contributed submissions, and interactive discussions will focus on the questions such as: How can modeling humans help us understand and design human-robot interactions? What kinds of models are useful for which HRI contexts (physical/cognitive interactions) and purposes (behavior prediction/personalization/theory -of- mind/etc.)? What common lessons can be learned from human behavior modeling in HRI across different application domains? How can modeling humans in HRI tasks help us to better understand human cognition/behavior? By stimulating an interdisciplinary conver-sation around these questions, we aim to raise awareness of the benefits of modeling and expose the wider HRI community to a variety of different modeling approaches, and facilitate the HRI researchers who already engage in modeling to exchange views on methodology of modeling and best nractices from diverse fields.

This paper describes the development of a 3- stage signalling framework to trigger a social robot's bottom- up reactive behavior inspired by a biological model. In the first stage, low-level firing of stimuli due to external sources is constructed through perception grounding. This is followed by a saliency classifier which fires-up high level salient signals that require attention and are used to trigger the robot's reactive behavior. The whole framework evolves primarily on the knowledge ontology that defines the characteristics of the social robot and the querying mechanism that correlates the perceived stimuli with the ontology to trigger the reactive behavior. We evaluated the performance of our system with timing metrics and we achieved good results for our application.

Explanations in artificial intelligence (AI) ensure that users of complex AI systems understand why the system behaves as it does. Expectations that users may have about the system behaviour play a role since they co-determine appropriate content of the explanations. In this paper, we investigate user-desired content of explanations when the system behaves in unexpected ways. Specifically, we presented participants with various scenarios involving an automated text classifier and then asked them to indicate their preferred explanation in each scenario. One group of participants chose the type of explanation from a multiple-choice questionnaire, the other had to answer using free text.

This article presents a discussion of the importance of power analyses, providing an overview of when power analyses should be run in the context of the field of Human-Robot Interaction, as well as some examples of how to perform a power analysis. This work was motivated by the observation that the majority of papers published in the proceedings of recent HRI conferences did not report conducting a power analysis; an observation that has concerning implications for many conclusions drawn by these studies. This work is intended to raise awareness and encourage researchers to conduct power analyses when designing research studies using human participants.

This paper reflects on four studies completed over the last 24 months, with social robots including Pepper, Paro, Joy for All cats and dogs, Miro, Pleo, Padbot and cheaper toys, including i) focus groups and interviews on suitable robot pet design, ii) surveys on ethical perceptions of robot pets, and iii) recorded interactions between stakeholders and a range of social robots. In total, up to 371 participants’ views were included across the analysed studies. Data was reviewed and mined for relevance to the use and impact of morphology types for social robots in health and social care. Results suggested biomorphic design was preferable over mechanomorphic, and speech and life-simulation features (such as breathing) were well received. Anthropomorphism demonstrated some limitations in evoking fear and task-expectations that were absent for zoomorphic designs. The combination of familiar, zoomorphic appearance with animacy, life-simulation and speech capabilities thus appeared to be an area of research for future robots developed for health and social care.

Research in the social sciences has shown that expectations are an important factor in explanations as used between humans: rather than explaining the cause of an event per se, the explainer will often address another event that did not occur but that the explainee might have expected. For AI-powered systems, this finding suggests that explanation-generating systems may need to identify such end user expectations. In general, this is a challenging task, not the least because users often keep them implicit; there is thus a need to investigate the importance of such an ability. In this paper, we report an empirical study with 181 participants who were shown outputs from a text classifier system along with an explanation of why the system chose a particular class for each text. Explanations were both factual, explaining why the system produced a certain output or counterfactual, explaining why the system produced one output instead of another. Our main hypothesis was explanations should align with end user expectations; that is, a factual explanation should be given when the system's output is in line with end user expectations, and a counterfactual explanation when it is not. We find that factual explanations are indeed appropriate when expectations and output match. When they do not, neither factual nor counterfactual explanations appear appropriate, although we do find indications that our counterfactual explanations contained at least some necessary elements. Overall, this suggests that it is important for systems that create explanations of AI systems to infer what outputs the end user expected so that factual explanations can be generated at the appropriate moments. At the same time, this information is, by itself, not sufficient to also create appropriate explanations when the output and user expectations do not match. This is somewhat surprising given investigations of explanations in the social sciences, and will need more scrutiny in future studies.

Theories on social learning indicate that imitative choices are usually performed whenever copying the others' behaviour has no additional cost. Here, we extended such investigations of social learning to Human-Robot Interaction (HRI). Participants played the Economic Investment Game with a robot banker while observing another robot player also investing in the robot banker. By manipulating the robot banker payoff, three conditions of unfairness were created: (1) unfair payoff for the participants, (2) unfair payoff for the robot player and (3) unfair payoff for both. Results showed that when the payoff was low for the participants and high for the robot player, participants invested more money in the robot banker than when both parties received a low return. Also, for this specific condition, participants' investments increased further with a more interactive robot player (defined as demonstrating increased attention, congruent movements and speech) This suggests that social and cognitive human competencies can be used and transposed to non-human agents. Further, imitation can potentially be extended to HRI, with interactivity likely having a key role in increasing this effect.

Human-robot interaction (HRI) is fundamentally concerned with studying the interaction between humans and robots. While it is still a relatively young field, it can draw inspiration from other disciplines studying human interaction with other types of agents. Often, such inspiration is sought from the study of human-computer interaction (HCI) and the social sciences studying human-human interaction (HHI). More rarely, the field also turns to human-animal interaction (HAI).In this paper, we identify two distinct underlying motivations for making such comparisons: to form a target to recreate or to obtain a benchmark (or baseline) for evaluation. We further highlight relevant (existing) overlap between HRI and HAI, and identify specific themes that are of particular interest for further trans-disciplinary exploration. At the same time, since robots and animals are clearly not the same, we also discuss important differences between HRI and HAI, their complementarity notwithstanding. The overall purpose of this discussion is thus to create an awareness of the potential mutual benefit between the two disciplines and to describe opportunities that exist for future work, both in terms of new domains to explore, and existing results to learn from.

Expressivity - the use of multiple, non-verbal, modalities to convey or augment the communication of internal states and intentions - is a core component of human social interactions. Studying expressivity in contexts of artificial agents has led to explicit considerations of how robots can leverage these abilities in sustained social interactions. Research on this covers aspects such as animation, robot design, mechanics, as well as cognitive science and developmental psychology. This workshop provides a forum for scientists from diverse disciplines to come together and advance the state of the art in developing expressive robots. Participants will discuss points of methodological opportunities and limitations, to develop a shared vision for next steps in expressive social robots.

Rapid technical advancements have led to dramatically improved abilities for artificial agents, and thus opened up for new ways of cooperation between humans and them, from disembodied agents such as Siris to virtual avatars, robot companions, and autonomous vehicles. It is therefore relevant to study not only how to maintain appropriate cooperation, but also where the responsibility for this resides and/or may be affected. While there are previous organisations and categorisations of agents and HAI research into taxonomies, situations with highly responsible artificial agents are rarely covered. Here, we propose a way to categorise agents in terms of such responsibility and agent autonomy, which covers the range of cooperation from humans getting help from agents to humans providing help for the agents. In the resulting diagram presented in this paper, it is possible to relate different kinds of agents with other taxonomies and typical properties. A particular advantage of this taxonomy is that it highlights under what conditions certain effects known to modulate the relationship between agents (such as the protégé effect or the "we"-feeling) arise.

The present workshop focuses on the topic of intention recognition in HRI. To be able to recognise intentions of other agents is a fundamental prerequisite to engage in, for instance, instrumental helping or mutual collaboration. It is a necessary aspect of natural interaction. In HRI, the problem is therefore bi-directional: not only does a robot need the ability to infer intentions of humans; humans also need to infer the intentions of the robot. From the human perspective, this inference draws both on the ability to attribute cognitive states to lifeless shapes, and the ability to understand actions of other agents through, for instance, embodied processes or internal simulations (i.e the human ability to form a theory of mind of other agents). How precisely, and to what degree these mechanisms are at work when interacting with social artificial agents remains unknown. From the robotic perspective, this lack of understanding of mechanisms underlying human intention recognition, or the capacity for theory of mind in general, is also challenging: the solution can, for instance, not simply be to make autonomous systems work “just like” humans by copying the biological solution and implementing some technological equivalent. It is therefore important to be clear about the theoretical framework(s) and inherent assumptions underlying technological implementations related to mutual intention. This remains very much an active research area in which further development is necessary. The core purpose of this workshop is thus to contribute to - and advance the state of the art in - this area.

Engagement is essential to meaningful social interaction between humans. Understanding the mechanisms by which we detect engagement of other humans can help us understand how we can build robots that interact socially with humans. However, there is currently a lack of measurable engagement constructs on which to build an artificial system that can reliably support social interaction between humans and robots. This paper proposes a definition, based on motivation theories, and outlines a framework to explore the idea that engagement can be seen as specific behaviors and their attached magnitude or intensity. This is done by the use of data from multiple sources such as observer ratings, kinematic data, audio and outcomes of interactions. We use the domain of human-robot interaction in order to illustrate the application of this approach. The framework further suggests a method to gather and aggregate this data. If certain behaviors and their attached intensities co-occur with various levels of judged engagement, then engagement could be assessed by this framework consequently making it accessible to a robotic platform. This framework could improve the social capabilities of interactive agents by adding the ability to notice when and why an agent becomes disengaged, thereby providing the interactive agent with an ability to reengage him or her. We illustrate and propose validation of our framework with an example from robot-assisted therapy for children with autism spectrum disorder. The framework also represents a general approach that can be applied to other social interactive settings between humans and robots, such as interactions with elderly people.