The identity design of artificial agents carries growing ethical, psychological, and cultural weight, as ubiquitous language models and diverse robotic forms are blended into everyday use. However, structured approaches to designing coherent and interpretable artificial identities remain limited. To address urgent challenges in artificial identity design, including harmful stereotypes and deceptive practices, we introduce the Identity Design (ID) Framework and an accompanying research agenda. Drawing on emerging work on artificial identity in human-robot interaction and taking an interdisciplinary perspective, we propose twelve design principles across three levels: individual (recognisability, behavioural consistency, identity continuity, memory, persistent goals), group (membership signalling, social alignment, role clarity), and societal (benevolence, artificiality, social justice, transparency). The research agenda outlines open questions around the operationalisation and measurement of identity, social dynamics, and ethical considerations for identity design. Together, they lay the groundwork for future research and responsible practice in robotic, virtual, and multi-embodied agents.

Methods for understanding the felt and situated experience of controlling autonomous systems are crucial for designing systems that are adjusted to the complexity of human interaction. Prior work has tended to overlook the bodily and experiential dimensions of monitoring systems at a distance, particularly in moments of losing control. We combined two approaches, ethnomethodology and conversation analysis, and soma design, to explore a case where a semi-autonomous system crashed when controlled by an inexperienced operator, captured in video ethnographic fieldwork. We report on our methodological approach, combining sequential video analysis of the unfolding sequence and interviews inspired by microphenomenology to unpack the operator’s experience of losing control. We contribute methodological considerations for interaction designers seeking to explore the felt experience of having and losing control of autonomous systems and discuss how insights gained through this combination of methods, rooted in phenomenology, support a designerly appreciation of safety operators’ work.

The “gender” of intelligent agents, virtual characters, social robots, and other agentic machines has emerged as a fundamental topic in studies of people’s interactions with computers. Perceptions of agent gender can help explain user attitudes and behaviours—from preferences to toxicity to stereotyping—across a variety of systems and contexts of use. Yet, standards in capturing perceptions of agent gender do not exist. A scoping review was conducted to clarify how agent gender has been operationalized—labelled, defined, and measured—as a perceptual variable. One-third of studies manipulated but did not measure agent gender. Norms in operationalizations remain obscure, limiting comprehension of results, congruity in measurement, and comparability for meta-analyses. The dominance of the gender binary model and latent anthropocentrism have placed arbitrary limits on knowledge generation and reified the status quo. We contribute a systematically-developed and theory-driven meta-level framework that offers operational clarity and practical guidance for greater rigour and inclusivity.

We present the concept of the auxthetic mind-body (AM): a system which extends the human mind and body to include robot bodies, artificial “thoughts,” and artificial feelings as part of the perception of “self.” While human-robot interaction research has long grappled with embodiment, the AM represents an as-yet unexplored space in this realm and raises a host of questions around its uses, consequences, and preservation of human agency. We explore the concept through speculative sociotechnical design, foregrounding how the technology might make us feel (rather than what it might do) as a guiding foundation for further development. Through poetry and marginalia, we invite readers to reflect on what it might mean to think, feel, and be with an AM. In doing so, we sketch both technical possibility and future worth longing for—one where the dissolution of the human-machine boundary can be meaningful, grounded, and less frightening than it may seem.

As a young and inherently interdisciplinary field, Human–Robot Interaction (HRI) shows evidence of multiple, competing and complementary epistemologies and methodologies. However, it is clear that HRI user studies are a valued, primary form of knowledge generation. How might this influence, and be influenced by choice of experimental robot platform? Once a clear, common platform of choice, the number of HRI conference papers detailing work utilising the NAO robot has consistently declined since 2015. We take this opportunity to present quantitative and qualitative data regarding the evolving use and role of the NAO robot in establishing what makes ‘good science’ in HRI, and to reflect on what this might mean for, and can tell us about, the field more broadly. We suggest that shared use of a common platform like NAO is emblematic of, and important for, the field’s attempts to stabilise. Access to a common platform supports sharing of knowledge and practices amongst researchers in the field, whilst at the same time produces a set of assumptions about what makes for (seemingly) scientific HRI knowledge, practice or ‘lore’ that will continue to shape the future of the field—even beyond the platform’s demise.

Perinatal depression (PND) is a common mental health disorder associated with childbirth, which has high societal costs affecting up to 10% of individuals during pregnancy or postpartum. Whilst socially assistive robots (SARs) have recently proven to be useful tools in mental healthcare, and our previous work has investigated different stakeholders’ perspectives on SARs in PND screening through interview studies, gaps remain in understanding how primary users (i.e., prospective patients) perceive and interact with such technologies. In this article, we use a participatory design methodology with semi-structured interviews of women in Sweden with previous experience of PND to explore the roles that SARs could play in addressing PND challenges and identify design factors for SARs in PND screening. We design and evaluate in a user study a robot prototype in two new interaction contexts for SARs with different levels of human oversight. The results show that SARs are welcomed by most participants, who appreciated the potentially faster assessment process and felt more comfortable opening up with a robot versus a human clinician. However, we found that there is no single solution that fits all, as other participants preferred the flexibility of self-reported digital surveys or interaction with a human clinician. Moreover, results show that transparency and human oversight are crucial requirements to consider when implementing robot-delivered PND screening questionnaires and diagnostic interviews. We reflect on ethical considerations, provide design recommendations and urge HRI designers to carefully consider whom SARs benefit, whom they may not, and which safeguarding factors are necessary to prevent potential negative outcomes.

The recent rise in popularity of large language models (LLMs) has prompted considerable concerns about their moral capabilities. Although considerable effort has been dedicated to aligning LLMs with human moral values, existing benchmarks and evaluations remain largely superficial, typically measuring alignment based on final ethical verdicts rather than explicit moral reasoning. In response, this paper aims to advance the investigation of LLMs’ moral capabilities by examining their capacity to function as Artificial Moral Assistants (AMAs), systems envisioned in the philosophical literature to support human moral deliberation. We assert that qualifying as an AMA requires more than what state-of-the-art alignment techniques aim to achieve: not only must AMAs be able to discern ethically problematic situations, they should also be able to actively reason about them, navigating between conflicting values outside of those embedded in the alignment phase. Building on existing philosophical literature, we begin by designing a new formal framework of the specific kind of behaviour an AMA should exhibit, individuating key qualities such as deductive and abductive moral reasoning. Drawing on this theoretical framework, we develop a benchmark to test these qualities and evaluate popular open LLMs against it. Our results reveal considerable variability across models and highlight persistent shortcomings, particularly regarding abductive moral reasoning. Our work connects theoretical philosophy with practical AI evaluation while also emphasising the need for dedicated strategies to explicitly enhance moral reasoning capabilities in LLMs.

“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.

This paper presents a participatory, qualitative focus group study with 13 young carers - young people between 13 to 18 years old who take care of a parent due to either a chronic illness, mental health problem, or other condition connected with a need for care in Wales and England. We identify and assert young carers as an important, thus far unconsidered, user group in Human-Robot Interaction (HRI). As such, this study is the first to explore this group's unique perspectives, highlighting their lived experiences and perceptions of care robots in the domestic setting. Our findings reveal the heterogeneity of this group, particularly regarding support for their caregiving roles and their ongoing use of technology. While participants saw social robots as having potential, especially for (i) time management, (ii) emotional and (iii) informational support, and (iv) monitoring their parent's health; concerns were raised about issues such as (1) malfunction, (2) limited range, (3) privacy and (4) cost. We distil our findings into some reflections on how future HRI research might better consider this important user group, including some methodological reflections on the practical, ethical and emotional challenges of undertaking this type of work.

Much Human-Robot Interaction (HRI) research is, in fact, speculative. The relatively far-future horizon of pervasive robot deployment means we are, in effect, designing for a best guess of what the world might look like in the future based on how it looks now. In doing so, we are contributing to the bringing about of that world over others. Drawing on notions of speculative design and sociotechnical imaginaries, this pictorial presents snippets of speculative, sociotechnical fiction writing, which (re-)imagine human-machine interactions in the socialist utopia future world of William Morris' 19thcentury novel “News from Nowhere”. Each fiction snippet is paired with references to the current academic literature that inspires it, in addition to some takeaway discussion points for the HRI community of today. My hope is to assert speculative, sociotechnical thinking as legitimate HRI design practice, and to demonstrate the value it can bring to our field.

Advancements in robotic capabilities for providing physical assistance, psychological support, and daily health management are making the deployment of intelligent health-care robots in home environments increasingly feasible in the near future. However, challenges arise when the information provided by these robots contradicts users’ memory, raising concerns about user trust and decision-making. This paper presents a study that examines how varying a robot’s level of transparency and sociability influences user interpretation, decision-making and perceived trust when faced with conflicting information from a robot. In a 2 × 2 between-subjects online study, 176 participants watched videos of a Furhat robot acting as a family healthcare assistant and suggesting a fictional user to take medication at a different time from that remembered by the user. Results indicate that robot transparency influenced users’ interpretation of information discrepancies: with a low transparency robot, the most frequent assumption was that the user had not correctly remembered the time, while with the high transparency robot, participants were more likely to attribute the discrepancy to external factors, such as a partner or another household member modifying the robot’s information. Additionally, participants exhibited a tendency toward overtrust, often prioritizing the robot’s recommendations over the user’s memory, even when suspecting system malfunctions or third-party interference. These findings highlight the impact of transparency mechanisms in robotic systems, the complexity and importance associated with system access control for multi-user robots deployed in home environments, and the potential risks of users’ over-reliance on robots in sensitive domains such as healthcare.

The Transformer architecture has seen a lot of attention in recent years also thanks to its ability to scale well and allow massive parallelism during training. This has made possible the development of Language Models (LMs) of increasing size and the discovery of latent abilities that completely outclass traditional methods e.g. rule-based systems. However, they also introduced new issues, like their inability to retain the history of previous interactions due to their stateless nature or the difficulty in controlling their generation. Different attempts have been made to address these issues, e.g. a `brute force' approach to solving the memory issue is to include the full conversation history in the context window, a solution that is limited by the quadratic scalability of Transformers. In this work, we explore computationally practical solutions to the memory problem. We propose to augment the decoder-only architecture of (most) Large LMs with a (relatively small) memory encoder. Its output is prepended to the decoder's input in a similar fashion to recent works in Adapters and the original Transformer architecture. Initial experiments show promising results, however future work is needed to compare with State-of-the-Art methods.

Domestic abuse research demonstrates that perpetrators are agile in finding new ways to coerce and to consolidate their control. They may leverage loved ones or cherished objects, and are increasingly exploiting and subverting what have become everyday 'smart' technologies. Robots sit at the intersection of these categories: they bring together multiple digital and assistive functionalities in a physical body, often explicitly designed to take on a social companionship role. We present a typology of robot facilitated abuse based on these unique affordances, designed to support systematic risk assessment, mitigation and design work. Whilst most obviously relevant to those designing robots for in-home deployment or intrafamilial interactions, the ability to coerce can be wielded by those who have any form of social power, such that our typology and associated design reflections may also be salient for the design of robots to be used in the school or workplace, between carers and the vulnerable, elderly and disabled and/or in institutions which facilitate intimate relations of care.

Young adults may feel embarrassed when disclosing sensitive information to their parents, while parents might similarly avoid sharing sensitive aspects of their lives with their children. How to design interactive interventions that are sensitive to the needs of both younger and older family members in mediating sensitive information remains an open question. In this paper, we explore the integration of large language models (LLMs) with social robots. Specifically, we use GPT-4 to adapt different Robot Communication Styles (RCS) for a social robot mediator designed to elicit self-disclosure and mediate health information between parents and young adults living apart. We design and compare four literature-informed RCS: three LLM-adapted (Humorous, Self-deprecating, and Persuasive) and one manually created (Human-scripted), and assess participant perceptions of Likeability, Usefulness, Helpfulness, Relatedness, and Interpersonal Closeness . Through an online experiment with 183 participants, we assess the RCS across two groups: adults with children (Parents) and young adults without children (Young Adults). Our results indicate that both Parents and Young Adults favoured the Human-scripted and Self-deprecating RCS as compared to the other two RCS. The Self-deprecating RCS furthermore led to increased relatedness as compared to the Humorous RCS. Our qualitative findings reveal challenges people have in disclosing health information to family members, and who normally assumes the role of family facilitator-two areas in which social robots can play a key role. The findings offer insights for integrating LLMs with social robots in health-mediation and other contexts involving the sharing of sensitive information.

Human-Robot Interaction (HRI) is inherently a human-centric field of technology. The role of feminist theories in related fields (e.g. Human-Computer Interaction, Data Science) are taken as a starting point to present a vision for Feminist HRI which can support better, more ethical HRI practice everyday, as well as a more activist research and design stance. We first define feminist design for an HRI audience and use a set of feminist principles from neighboring fields to examine existent HRI literature, showing the progress that has been made already alongside some additional potential ways forward. Following this we identify a set of reflexive questions to be posed throughout the HRI design, research and development pipeline, encouraging a sensitivity to power and to individuals' goals and values. Importantly, we do not look to present a definitive, fixed notion of Feminist HRI, but rather demonstrate the ways in which bringing feminist principles to our field can lead to better, more ethical HRI, and to discuss how we, the HRI community, might do this in practice.

Previous works in Human-Robot Interaction have demonstrated the positive potential benefit of designing social robots which express specific personalities. In this work, we focus specifically on the adaptation of language (as the choice of words, their order, etc.) following the extraversion trait. We look to investigate whether current language models could support more autonomous generations of such personality-expressive robot output. We examine the performance of two models with user studies evaluating (i) raw text output and (ii) text output when used within multi-modal speech from the Furhat robot. We find that the ability to successfully manipulate perceived extraversion sometimes varies across different dialogue topics. We were able to achieve correct manipulation of robot personality via our language adaptation, but our results suggest further work is necessary to improve the automation and generalisation abilities of these models.

With the deployment of robots in public realms, researchers are seeing more and more cases of abusive disinhibition towards robots. Because robots embody gendered identities, poor navigation of antisocial dynamics may reinforce or exacerbate gender-based violence. Robots deployed in social settings must recognize and respond to abuse in a way that minimizes ethical risk. This will require designers to first understand the risk posed by abuse of robots, and how humans perceive robot-directed abuse. To that end, we conducted an exploratory study of reactions to a physically abusive interaction between a human perpetrator and a victimized agent. Given extensions of gendered biases to robotic agents, as well as associations between an agent’s human likeness and the experiential capacity attributed to it, we quasi-manipulated the victim’s humanness (via use of a human actor vs. NAO robot) and gendering (via inclusion of stereotypically masculine vs. feminine cues in their presentation) across four video-recorded reproductions of the interaction. Analysis of data from 417 participants, each of whom watched one of the four videos, indicates that the intensity of emotional distress felt by an observer is associated with their gender identification, previous experience with victimization, hostile sexism, and support for social stratification, as well as the victim’s gendering.

Although the field of Explainable Artificial Intelligence (XAI) in Human-Robot Interaction is gathering increasing attention, how well different explanations compare across HRI scenarios is still not well understood. We conducted an exploratory online study with 335 participants analysing the interaction between type of explanation (counterfactual, feature-based, and no explanation), the stake of the scenario (high, low) and the application scenario (healthcare, industry). Participants viewed one of 12 different vignettes depicting a combination of these three factors and rated their system understanding and trust in the robot. Compared to no explanation, both counterfactual and feature-based explanations improved system understanding and performance trust (but not moral trust). Additionally, when no explanation was present, high-stake scenarios led to significantly worse performance trust and system understanding. These findings suggest that explanations can be used to calibrate users’ perceptions of the robot in high-stake scenarios.

We present the results of an online, video-based experimental study investigating the impact of robot agency on perceptions of a socially assistive robot (SAR) shown supporting in-home care. We consider two key participant groups: care givers and care receivers. We did not find significant results regarding the impact of agency on overall participant perceptions of the SAR, but we did identify some differences in what these two participant groups might perceive as being best for themselves versus each other. Firstly, care givers perceived more potential benefit from the robot than care receivers did, challenging possible assumptions about who is set to gain most from deployment of these systems. Secondly, care receivers generally perceived the lower agency robot as being more beneficial for themselves, even as they ascribed the higher agency robot more potential to benefit care receivers.

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.

The Participatory Design and End-User Program-ming for Human-Robot Interaction (HRI) workshop aims to advance research on how to design systems that can be used by end users to program robots. There tends to be a fracture in HRI between the technical designers of robot programs (often engineers or computer scientists) and the actual users of such robots. Developers have the capabilities to program robots but often lack insights possessed by domain experts, sometimes leading to technically interesting but impractical systems. With this workshop, we aim to bridge two different methods often used individually within the wider HRI community to involve end users in robot program design: Participatory Design (PD) and End-User Programming (EUP). Both methods empower end users to co-produce robots addressing real-world needs. However, there have been limited opportunities to unite researchers who specialize in these areas and engage in mutual learning. We will address this shortcoming with a full-day workshop, which will put the PD and EUP communities in touch, inviting speakers from both sides and welcoming a wide range of publications from describing new end-user programming methods to compiling insights learned from conducting participatory design studies.

This article presents a cross-cultural replication of recent work on productively violating gender norms; specifically demonstrating that breaking norms can boost robot credibility while avoiding harmful stereotypes. In this work we demonstrate via a 3 (country) x 3 (robot behaviour) between-subject experiment that these findings replicate cross-culturally across the US, Sweden, and Japan, finding evidence that breaking gender norms boosts robot credibility regardless of gender or cultural context, and regardless of pretest gender biases. Our findings further motivate a call for feminist robots that subvert the existing gender norms of robot design.

Successful, enjoyable group interactions are important in public and personal contexts, especially for teenagers whose peer groups are important for self-identity and self-esteem. Social robots seemingly have the potential to positively shape group interactions, but it seems difficult to effect such impact by designing robot behaviors solely based on related (human interaction) literature. In this article, we take a user-centered approach to explore how teenagers envisage a social robot "group assistant". We engaged 16 teenagers in focus groups, interviews, and robot testing to capture their views and reflections about robots for groups. Over the course of a two-week summer school, participants co-designed the action space for such a robot and experienced working with/wizarding it for 10+ hours. This experience further altered and deepened their insights into using robots as group assistants. We report results regarding teenagers’ views on the applicability and use of a robot group assistant, how these expectations evolved throughout the study, and their repeat interactions with the robot. Our results indicate that each group moves on a spectrum of need for the robot, reflected in use of the robot more (or less) for ice-breaking, turn-taking, and fun-making as the situation demanded.

In this paper we apply the recent concept of robot Ethical Risk Assessment to an exemplar Socially Assistive Robot (SAR); specifically considering ethical risks posed by anthropomorphism in this context. We draw on two complimentary studies to demonstrate that anthropomorphism is important to overall SAR function and overall relatively low ethical risk. As such, rather than avoiding anthropomoprhism all together (as suggested in a recently published standard on robot ethics), we suggest anthropomorphism in SARs should be a customisable trait that can be adapted to the user.

Nowadays, off-the-shelf social robots are used more frequently by the HRI community to research social interactions with different types of users across a range of domains such as education, retail, health care, public places and other domains. Everyone doing HRI research with end-users is invited to submit a case study to our workshop. We are particularly interested in case studies where things did not go as planned. Case studies describing research in the lab or in the wild are both welcome. Examples of unplanned experiences could include, but are not limited to, unexpected responses from the user, issues with the experimental setup or simply having challenges with transferring theory to the real world. In this workshop, we focus on off-the-shelf robots. In order to generalize and compare differences across multiple HRI domains and create common solutions, we will provide a template for your case study. We are interested in learning how such unexpected HRI results can be reported. In the workshop, we will discuss and study how failures are reported and be inspired to create a list of good ways to report failures, which can hopefully be inspiring for the HRI community.

Social influence refers to an individual's attitudes and/or behaviours being influenced by others, whether implicit or explicit, such that persuasion and compliance gaining are instances of social influence [1] [2]. In human-human interaction (HHI), the desire to understand compliance and maximise social influence for persuasion has led to the development of theory and resulting strategies one can use in an attempt to leverage social influence, e.g. Cialdini's 'Weapons of Influence' [3]. Whilst a number of social human-robot interaction (HRI) studies have investigated the impact of different robot behaviours in compliance gaining/persuasion (e.g. [4]-[7]); established strategies for maximising this are yet to emerge, and it is unclear to what extent theories and strategies from HHI might apply.

In this paper we present the results of an experimental study investigating the application of human persuasive strategies to a social robot. We demonstrate that robot displays of goodwill and similarity to the participant significantly increased robot persuasiveness, as measured objectively by participant behaviour. However, such strategies had no impact on subjective measures concerning perception of the robot, and perception of the robot did not correlate with participant behaviour. We hypothesise that this is due to difficulty in accurately measuring perception of a robot using subjective measures. We suggest our results are particularly relevant for the design and development of socially assistive robots.

In this paper we present the results of a qualitative study with therapists to inform social robotics and human robot interaction (HRI) for engagement in rehabilitative therapies. Our results add to growing evidence that socially assistive robots (SARs) could play a role in addressing patients' low engagement with self-directed exercise programmes. Specifically, we propose how SARs might augment or offer more pro-active assistance over existing technologies such as smartphone applications, computer software and fitness trackers also designed to tackle this issue. In addition, we present a series of design implications for such SARs based on therapists' expert knowledge and best practices extracted from our results. This includes an initial set of SAR requirements and key considerations concerning personalised and adaptive interaction strategies.

This extended abstract gives an overview of current doctoral research being undertaken on human robot interaction (HRI) for social robots to be used as motivation and engagement tools in rehabilitative therapies, e.g. physiotherapy, occupational therapy and speech therapy. Specifically the work aims to design the AI, social behaviours and interaction modalities for such a robot, validated through a series of human human interaction (HHI) and HRI studies.

In this paper we investigate robot to human Interpersonal Emotion Transfer (IET) in a real world contextualised human-robot interaction (HRI). IET is an umbrella term which describes the impact of emotions in human-human interaction (HHI). This includes emotion contagion and social appraisal effects. These effects are particularly relevant in domains such as teaching, sports, exercise and healthy eating; domains increasingly targeted by socially assistive robotics. As such, we suggest socially assistive robots may benefit from affective communication in the same way as their human counterparts. We show that emotion recognition from robot voice and motion is possible in explicit validation experiments but does not hold in a socially assistive interaction. Our findings suggest that robot to human IET relies on the human having an expectation for, and hence recognition of, robot emotions; mimicry of valanced motion is not sufficient.