Human–Robot Interaction (HRI) research is starting to engage with sustainability, yet the field remains tied to economic models that assume continual growth, rapid technological development, and market expansion. This economic growth orientation raises questions about whether HRI can genuinely support ecological responsibility, given the resource intensity of robotics research, production, and deployment. In this contribution, we introduce a post-growth perspective to reframe the relationship between robotics, sustainability, and society. We argue that rather than striving for `green growth' within existing economic structures, HRI should engage critically with concepts such as degrowth and post-capitalism. By shifting attention from growth to development, we invite the community to consider what robotic futures are worth pursuing and for whom.

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.

This full-day workshop focuses on advancing research and development in socially-competent agents that care. Many agent platforms and kinds of embodiments of agents that have a primary focus on caring, one way or another, for the end users. In this workshop, we aim to look at value-based design, inclusiveness, empathetic design and long-term human-agent interaction for socially competent agents that care.

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.

Welfare technology has recently reached older adults in long-term care facilities (LTCFs).Many Swedish municipalities are introducing emerging technologies such as virtual reality, robotic assistive devices, and social robots in LTCFs as part of everyday care.However, not only older adults are affected by these deployments.Healthcare workers are left to master these technologiesand integrate them into existing care practices.Previous research has identified an increase in work-related stress associated with the introduction of technology for healthcare workers.The literature is, however, sparse on how healthcare workers in LTCFs are affected by the introduction.Therefore, we explored different factors that could affect healthcare workers' technostress through an online survey and semi-structured interviews to get a deeper understanding of how healthcare workers are experiencing deployments of welfare technology.The main findings showed that some of the healthcare workers are finding it difficult to adopt and use welfare technology due to, for example, older age, language difficulties, or a negative attitude toward technology.We conclude that municipalities and LTCFs need to invest in their healthcare workers in order to achieve better on-boarding and reduce technostress.

During the past decade, there has been an increased interest in using social companionship robots for older adults in care homes. Previous studies have shown that these robots, often in the shape of a household pet, can decrease stress and loneliness while increasing communication and quality of life. In our research, studying the effects of cat and dog robots at care homes, we got the impression that these robots are quite common and that the reason for implementing them varies. Therefore, we conducted an online survey, asking all municipalities in Sweden if they have social companionship robots, how they are using them and for what kind of care organisation. The result showed that more than half of the municipalities use pet robots for older adults, and most commonly for people with dementia to lower stress and increase the feeling of safety.

Robot’s persuasive abilities have previously shown contradictory results with some depending on robot gender and some on user gender. Therefore, we conducted a replication study with the Furhat robot. The study measured differences in how persuasive (ethos, pathos, and logos) a more feminine and a more masculine looking robot was perceived by female or male participants. We hypothesised that a platform with both feminine/masculine faces and voices enables larger differences between the robot’s persuasiveness compared to a female/male NAO robot (original study). Results showed statistically significant differences regarding persuasiveness between participant gender but none for the robot gender. One difference, compared to the original study, was that men rated ethos higher than women did.

With more robots entering social environments, such as care homes for older adults, it is increasingly important to understand the target user groups’ perception of different robot platforms that are introduced into their immediate surroundings. To add to existing research on older adults’ attitudes toward robots after meeting a robot, and social acceptance toward certain robots, we conducted a short-term interaction study with the Furhat robot. Furhat, a blended embodiment of a physical robot head with a virtual (back-projected) face, is being marketed as one of the most social robots with high human-likeness. However, this study’s results indicates that older adults do not perceive Furhat as anthropomorphic or sentient, and they especially reported a negative attitude toward robots with emotions after meeting Furhat. Despite these scores, all participants were engaged in the interaction with the robot. However, our results differ from previous studies by indicating that older adults have relatively low social acceptance for Furhat and a relatively negative attitude toward robots after meeting Furhat.

During the past year with a pandemic, there have been many discussions of the use of robots in healthcare, and in particular the care of older adults. There are reports that COVID-19 drives adoption of robot technologies, and that health institutions are more positive to the use of robots due to the lower risk of virus spread, among other reasons. The media have contributed significantly to an increased interest in caring robots and from a societal perspective — given recent lockdowns, social distancing, etc. — an increase of robot usage in health care settings is not difficult to imagine. Older adults have in many countries been isolated, either at home or in care homes. This group, and especially individuals with dementia, were already vulnerable before the pandemic, living with an increased risk for loneliness and depression. For the last 10 years in Sweden, many actions have been taken to improve these people’s well-being and quality of life, including the use of social companion robots as psychological interventions. Given the increased isolation due to the pandemic, we wanted to investigate how the use of social companion robots has been affected in care homes during this time. We, therefore, interviewed nine health care staff members from seven different care homes for older adults in Sweden. The results summarised in this paper provides a real-world status report, one year after the outbreak, of how the pandemic has affected the usage of social companion robots in care homes.

In an explorative study concerning the social acceptance of two specific humanoid robots, the experimenter asked participants (N = 36) to place a book in an adjacent room. Upon entering the room, participants were confronted by a NAO or a Pepper robot expressing persistent opposition against the idea of placing the book in the room. On average, 72% of participants facing NAO complied with the robot's requests and returned the book to the experimenter. The corresponding figure for the Pepper robot was 50%, which shows that the two robot morphologies had a different effect on participants' social behavior. Furthermore, results from a post-study questionnaire (GODSPEED) indicated that participants perceived NAO as more likable, intelligent, safe and lifelike than Pepper. Moreover, participants used significantly more positive words and fewer negative words to describe NAO than Pepper in an open-ended interview. There was no statistically significant difference between conditions in participants' negative attitudes toward robots in general, as assessed using the NARS questionnaire.