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.

Human-robot interaction (HRI) programmers often struggle with operating older robot hardware due to the short support period provided by manufacturers and difficulties integrating modern software solutions. This paper introduces the ZTL Task Library (ZTL), a lightweight communication framework and protocol designed to decouple robot hardware from the operating platform via socket communication, thereby increasing robot lifetime. We present a task-based communication protocol facilitating the co-design of robot behaviours with non-programming experts. Our approach has been shown across different platforms to effectively mitigate incompatibilities between middlewares, simplifying control and usability, allowing for simultaneous addressing of multiple devices.

There are a multitude of robotic application domains that touch on the field of human-robot interaction (HRI). From modern manufacturing involving human-robot teams, to personal care robots assisting the elderly, the roles that robots are being tasked with and the nature of interactions with humans are constantly shifting. Even the nature of interaction has changed to incorporate wearable technologies such as exoskeletons to enhance human capabilities, and advanced prosthetics to restore those abilities that have been lost. With this ever-evolving spectrum of HRI, the capacity of measurement science to evaluate, assess, and assure performance and safety struggles to keep up. Building on our previous five-workshop series on Test Methods and Metrics for Effective HRI, NIST presents a new series on evaluative methodologies for accelerating the pipeline from cutting-edge HRI research to state-of-practice. This workshop will address issues regarding 1) data collection and reporting for replicability and system validation, 2) test design and execution for performance verification, and 3) cross-modality artifact design for real-world application-adjacent technology transfer. The goal of this workshop is to accelerate and accommodate accessibility to HRI research results, and address the specific key performance indicators that would establish end-user trust and acceptance of emerging HRI technologies.

In an interdisciplinary and evolving research field like human-robot interaction, clear and precise results reporting is essential for study comparability and replicability. To address the lack of a standard for such reporting and, at the same time, provide guidance for novices in the field, we have developed a web-based reporting form to capture human-robot interaction studies, serving as a model for how conferences could adopt it into the submission pipeline. In this work, we present a formative evaluation of this form regarding its level of detail, format and clarity, and the perceived benefits for authors, reviewers, and the community as a whole. We report the expert review of nine researchers who highlight the substantial value of this tool. In addition, these experts also provide suggestions for improvements to its form and the addition of details surrounding qualitative reporting.

This paper explores the elements that are needed to facilitate the seamless transfer of a robotic agent’s "persona" between various embodiments. It addresses the challenges in maintaining the robot identity, user trust, and engagement during transitions into other robot embodiments. Through a literature review, we propose a framework for decomposing and reconstructing a robot’s persona across embodiments, integrating visual, audial, and behavioral identity signals. By leveraging insights from robot interaction studies, this research contributes to the design of transferable and adaptable robot companions, with applications in eldercare and assistive technologies. The findings have broader implications for advancing human-robot interaction and fostering sustainable, user-centered robotic systems.

Trust plays a major role when introducing interactive robots into people’s personal spaces, which, in large part, depends on how they perceive the robot. This paper presents the initial results of an investigation into the perception of robot agency as a potential factor influencing trust. We manipulated a robot’s agency to see how trust would change as a result. Our preliminary results indicate age as a confounding factor while we did not find differences when priming robot autonomy.

Fluidity is a key quality of human-human and more natural human-agent interaction (HAI). The concept of fluidity is difficult to define formally, however, interaction partners and users perceive the difference between more and less fluid interaction. As an initial informal definition, fluidity in interaction can be considered the abilities to seamlessly transition in turn-taking, to allow appropriate overlap of turns between agents, including multimodally, and to allow action using prediction. The purpose of this workshop is to bring agent designers together to attempt to define fluidity in interaction more precisely and propose ways in which we can make HAI more fluid.

Achieving truly fluid interaction with robots with speech interfaces remains a hard problem. Despite technical advances in sensors, processors and actuators, the experience of Human-Robot Interaction (HRI) remains laboured and frustrating. Some of the barriers to this stem from a lack of a suitable development platform for HRI to improve the interaction, particularly for mobile manipulator robots. In this paper we briefly overview some existing systems and propose a high-fidelity Virtual Reality (VR) HRI simulation environment with Wizard-of-Oz (WoZ) cabability applicable to multiple robots including mobile manipulators and social robots.

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human–Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in CL, however, has been robot-centered to develop CL algorithms that can quickly learn new information on systematically collected static datasets. In this article, we take a human-centered approach to CL, to understand how humans interact with, teach, and perceive CL robots over the long term, and if there are variations in their teaching styles. We developed a socially guided CL system that integrates CL models for object recognition with a mobile manipulator robot and allows humans to directly teach and test the robot in real time over multiple sessions. We conducted an in-person study with 60 participants who interacted with the CL robot in 300 sessions with 5 sessions per participant. In this between-participant study, we used three different CL models deployed on a mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. Our analysis shows that the constrained experimental setups that have been widely used to test most CL models are not adequate, as real users interact with and teach CL robots in a variety of ways. Finally, our analysis shows that although users have concerns about CL robots being deployed in our daily lives, they mention that with further improvements CL robots could assist older adults and people with disabilities in their homes.

We present a new typology for classifying signals from robots when they communicate with humans. For inspiration, we use ethology, the study of animal behaviour and previous efforts from literature as guides in defining the typology. The typology is based on communicative signals that consist of five properties: the origin where the signal comes from, the deliberateness of the signal, the signal's reference, the genuineness of the signal, and its clarity (i.e., how implicit or explicit it is). Using the accompanying worksheet, the typology is straightforward to use to examine communicative signals from previous human-robot interactions and provides guidance for designers to use the typology when designing new robot behaviours.

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been robot-centered to develop continual learning algorithms that can quickly learn new information on static datasets. In this paper, we take a human-centered approach to continual learning, to understand how humans teach continual learning robots over the long term and if there are variations in their teaching styles. We conducted an in-person study with 40 participants that interacted with a continual learning robot in 200 sessions. In this between-participant study, we used two different CL models deployed on a Fetch mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. The results also show that although there is a difference in the teaching styles between expert and non-expert users, the style does not have an effect on the performance of the continual learning robot. Finally, our analysis shows that the constrained experimental setups that have been widely used to test most continual learning techniques are not adequate, as real users interact with and teach continual learning robots in a variety of ways. Our code is available at https://github. com/aliayub7/c1-hri.

This paper presents an investigation into the effectiveness of introducing explicit causal explanations in a child-robot interaction setting to help children with autism improve their Visual Perspective Taking (VPT) skills. A sample of ten children participated in three sessions with a social robot on different days, during which they played several games consisting of VPT tasks. In some of the sessions, the robot provided constructive feedback to the children by giving causal explanations related to VPT; other sessions were control sessions without explanations. An analysis of the children’s learning progress revealed that they improved their VPT abilities faster when the robot provided causal explanations. However, both groups ultimately reach a similar ratio of correct answers in later sessions. These findings suggest that providing causal explanations using a social robot can be effective to teach VPT to children with autism. This study paves the way for further exploring a robot’s ability to provide causal explanations in other educational scenarios.

The research presented in this paper investigates the feasibility of using humanoid robots like Kaspar as assistive tools in Speech, Language and Communication (SLC) therapy for children with learning disabilities. The study aims to answer two research questions: RQ1. Can a social robot be used to improve SLC skills of children with learning disabilities? RQ2. What is the measurable impact of interacting with a humanoid robot on children with learning disability and SLC needs? A co-creation approach was followed, three therapeutic educational games were developed and implemented on the Kaspar robot in collaboration with experienced SLC experts. Twenty children from two different special educational needs schools participated in the games in 9 sessions over a period of 3 weeks. Results showed significant improvement in participants’ SLC skills – i.e. language comprehension and production skills– over the intervention. Findings of this research affirms feasibility, suggesting that this type of robotic interaction is the right path to follow to help the children improve their SLC skills.

Children with Autism Spectrum Disorder (ASD) often struggle with visual perspective taking (VPT) skills and the understanding that others might have viewpoints and perspectives that are different from their own; i.e., the ability to understand that two or more people looking at the same object from different positions might not see the same thing. The understanding of VPT can be improved by introducing explicit causal explanations in the interactions involving autistic children. Moreover, the use of social robots can help autistic children improve their social skills. We present a retrospective study with Kaspar, a humanoid social robot specifically designed to interact with children with ASD, which aims to define the initial protocol for a study on the effect of causal explanation in VPT provided by Kaspar. To this end, we investigate in which scenarios causal explanations, provided either by researchers or by Kaspar, contribute substantially to the child’s understanding of VPT. The results have helped us identify multiple interaction categories that benefit from causal explanation. We have used these results in order to define new interaction games that benefit from causal explanations. These are now progressing through usability assessment experiments.

Humans need to understand and trust the robots they are working with. We hypothesize that how a robot moves can impact people's perception and their trust. We present a methodology for a study to explore people's perception of a robot using the animation principle of slow in, slow out-to change the robot's velocity profile versus a robot moving using a linear velocity profile. Study participants will interact with the robot within a home context to complete a task while the robot moves around the house. The participants' perceptions of the robot will be recorded using the Godspeed Questionnaire. A pilot study shows that pilot participants notice the difference between the linear and the slow in, slow out velocity profiles, so the full experiment planned with participants will allow us to compare their perceptions based on the two observable behaviors.

We investigated how a robot moving with different velocity profiles affects a person's perception of it when working together on a task. The two profiles are the common linear profile and a profile based on the animation principles of slow in, slow out. The investigation was accomplished by running an experiment in a home context where people and the robot cooperated on a clean-up task. We used the Godspeed series of questionnaires to gather people's perception of the robot. Average scores for each series appear not to be different enough to reject the null hypotheses, but looking at the component items provides paths to future areas of research. We also discuss the scenario for the experiment and how it may be used for future research into using animation techniques for moving robots and improving the legibility of a robot's locomotion.

This work investigates how human awareness about a social robot's capabilities is related to trusting this robot to handle different tasks. We present a user study that relates knowledge on different quality levels to participant's ratings of trust. Secondary school pupils were asked to rate their trust in the robot after three types of exposures: a video demonstration, a live interaction, and a programming task. The study revealed that the pupils' trust is positively affected across different domains after each session, indicating that human users trust a robot more the more awareness about the robot they have.

Technologies that aim to achieve intelligent automation in smart homes typically involve either trigger-action pairs or machine learning. These, however, are often complex to configure or hard to comprehend for the user. To maximize automation efficiency while keeping the configuration simple and the effects comprehensible, we thus explore an alternative agent-based approach. With the help of a survey, we put together a set of intelligent agents that act autonomously in the environment. Conflicts between behaviors, identified with a secondary study, are thereby resolved with a competitive combination of agents. We finally present the draft of a user interface that allows for individual configuration of all agents.

The first workshop on embodied interaction with smart environments aims to bring together the very active community of multi-modal interaction research and the rapidly evolving field of smart home technologies. Besides addressing the software architecture of such very complex systems, it puts an emphasis on questions regarding an intuitive interaction with the environment. Thereby, especially the role of agency leads to interesting challenges in the light of user interactions. We therefore encourage a lively discussion on the design and concepts of social robots and virtual avatars as well as innovative ambient devices and their implementation into smart environments.

In this demonstration, a humanoid robot interacts with an interlocutor through speech and gestures in order to give directions on a map. The interaction is specifically designed to provide an enhanced user experience by being aware of non-verbal social signals. Therefore, we take spatial communicative cues into account and to react to them accordingly.

This work-in-progress paper presents an on-line system for robotic heads capable of mimicking humans. The marker-less method solely depends on the interactant's face as an input and does not use a set of basic emotions and is thus capable of displaying a large variety of facial expressions. A preliminary evaluation assigns solid performance with potential for improvement.

In this paper, we present a first step towards a typology of relevant signals and cues in human-robot interaction (HRI). In human as well as in animal communication systems, signals and cues play an important role for senders and receivers of such signs. In our typology, we systematically distinguish between a robot's signals and cues which are either designed to be human-like or artificial to create meaningful information. Subsequently, developers and designers should be aware of which signs affect a user's judgements on social robots. For this reason, we first review several signals and cues that have already been successfully used in HRI with regard to our typology. Second, we discuss crucial human-like and artificial cues which have so far not been considered in the design of social robots - although they are highly likely to affect a user's judgement of social robots.