Understanding user enjoyment is crucial in human-robot interaction (HRI), as it can impact interaction quality and influence user acceptance and long-term engagement with robots, particularly in the context of conversations with social robots. However, current assessment methods rely solely on self-reported questionnaires, failing to capture interaction dynamics. This work introduces the Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES), a novel 5-point scale to assess user enjoyment from an external perspective (e.g.by an annotator) for conversations with a robot. The scale was developed through rigorous evaluations and discussions among three annotators with relevant expertise, using open-domain conversations with a companion robot that was powered by a large language model, and was applied to each conversation exchange (i.e.a robot-participant turn pair) alongside overall interaction. It was evaluated on 25 older adults' interactions with the companion robot, corresponding to 174 minutes of data, showing moderate to good alignment between annotators. Although the scale was developed and tested in the context of older adult interactions with a robot, its basis in general and non-task-specific indicators of enjoyment supports its broader applicability. The study further offers insights into understanding the nuances and challenges of assessing user enjoyment in robot interactions, and provides guidelines on applying the scale to other domains and populations. The dataset is available online.

We explore how laughter functions in in-the-wild human–Alexa interactions recorded in domestic settings. To do so, we analysed all instances of laughter in a corpus containing audio recordings from six households where an Alexa device had been newly acquired. The participants had no or very limited prior experience with voice assistants. Their interactions with Alexa were recorded over the first seven to ten weeks of use. Unlike previous HRI studies that primarily focus on dyadic, task-based exchanges, our analysis reveals that laughter in these real-world settings often emerges in multiparty interactions and serves a range of social functions beyond direct responses to the device. These observations highlight not only the need for ecologically grounded models of laughter in human–robot interaction, but also the value of linguistic and interactional analysis in uncovering the nuanced communicative roles laughter plays in everyday technology use. Such an approach allows us to identify how laughter signals both matches and mismatches in communication by marking alignment, managing breakdowns, and negotiating social meaning in interactions that often involve more than just the user and the device.

“Sustainability −4- HRI, HRI −4-Sustainability” offers hands-on engagement with the HRI 2025 conference theme, “Robots for a Sustainable World”. This workshop will explore the relationship between HRI and sustainable development and stimulate discussion on how we can make our own research practices more sustainable. We propose a full-day workshop, featuring morning discussions with sustainability experts and activists, and an afternoon hands-on activity aimed at understanding how robotics research can help in creating sustainable futures. We will broadly answer the following questions: How can we, as robotic researchers, help address sustainable development responsibly? Equally, how can we ensure that our HRI research practices minimises ecological footprints and operates within ethical and sustainable frameworks? We envision two practical outcomes of this workshop: a “sustainability statement” that can be submitted together with future HRI research papers, and a paper gathering insights and reflections from the workshop. We welcome researchers and students at any career stage and from any subfield of HRI to attend and contribute.

Enjoyment is a crucial yet complex indicator of positive user experience in Human-Robot Interaction (HRI). While manual enjoyment annotation is feasible, developing reliable automatic detection methods remains a challenge. This paper investigates a multimodal approach to automatic enjoyment annotation for HRI conversations, leveraging large language models (LLMs), visual, audio, and temporal cues. Our findings demonstrate that both text-only and multimodal LLMs with carefully designed prompts can achieve performance comparable to human annotators in detecting user enjoyment. Furthermore, results reveal a stronger alignment between LLM-based annotations and user self-reports of enjoyment compared to human annotators. While multimodal supervised learning techniques did not improve all of our performance metrics, they could successfully replicate human annotators and highlighted the importance of visual and audio cues in detecting subtle shifts in enjoyment. This research demonstrates the potential of LLMs for real-time enjoyment detection, paving the way for adaptive companion robots that can dynamically enhance user experiences.

The Negative Attitude toward Robots Scale (NARS) is one of the most common questionnaires used in the studies of human-robot interaction (HRI). It was established in 2004, and has since then been used in several domains to measure attitudes, both as main results and as a potential confounding factor. To better understand this important tool of HRI research, we reviewed the HRI literature with a specific focus on practice and reporting related to NARS. We found that the use of NARS is being increasingly reported, and that there is a large variation in how NARS is applied. The reporting is, however, often not done in sufficient detail, meaning that NARS results are often difficult to interpret, and comparing between studies or performing meta-analyses are even more difficult. After providing an overview of the current state of NARS in HRI, we conclude with reflections and recommendations on the practices and reporting of NARS.

When deploying robots, its physical characteristics, role, and tasks are often fixed. Such factors can also be associated with gender stereotypes among humans, which then transfer to the robots. One factor that can induce gendering but is comparatively easy to change is the robot’s voice. Designing voice in a way that interferes with fixed factors might therefore be a way to reduce gender stereotypes in human-robot interaction contexts. To this end, we have conducted a video-based online study to investigate how factors that might inspire gendering of a robot interact. In particular, we investigated how giving the robot a gender-ambiguous voice can affect perception of the robot. We compared assessments (n=111) of videos in which a robot’s body presentation and occupation mis/matched with human gender stereotypes. We found evidence that a gender-ambiguous voice can reduce gendering of a robot endowed with stereotypically feminine or masculine attributes. The results can inform more just robot design while opening new questions regarding the phenomenon of robot gendering.

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.

Recent work identified a concerning trend of disproportional gender representation in research participants in Human–Computer Interaction (HCI). Motivated by the fact that Human–Robot Interaction (HRI) shares many participant practices with HCI, we explored whether this trend is mirrored in our field. By producing a dataset covering participant gender representation in all 684 full papers published at the HRI conference from 2006–2021, we identify current trends in HRI research participation. We find an over-representation of men in research participants to date, as well as inconsistent and/or incomplete gender reporting, which typically engages in a binary treatment of gender at odds with published best practice guidelines. We further examine if and how participant gender has been considered in user studies to date, in-line with current discourse surrounding the importance and/or potential risks of gender based analyses. Finally, we complement this with a survey of HRI researchers to examine correlations between who is doing with the who is taking part, to further reflect on factors which seemingly influence gender bias in research participation across different sub-fields of HRI. Through our analysis, we identify areas for improvement, but also reason for optimism, and derive some practical suggestions for HRI researchers going forward.

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.

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.