As autonomous vehicles (AVs) emerge as new agents in traffic, it becomes imperative to understand how they can be introduced effectively and acceptably. AVs enter existing socio-technical systems, where members of various social communities are already present, including residents rushing to work, tourists strolling around, and workers cleaning the streets. This workshop seeks to explore the intricate dynamics of introducing AVs into established social systems within shared spaces. It aims to understand what the needs of pedestrians, cyclists, drivers, and passengers are, and how they interact with each other when AVs enter the social system. Through presentations and interactive activities, this workshop will provide a platform for researchers, practitioners, and other stakeholders to exchange insights, share experiences, and co-create strategies relevant to the integration of AVs into the social systems of shared spaces. The workshop aims to discuss the benefits, risks, and design challenges associated with integrating this emerging technology into our everyday lives.

Increasing encounters between people and autonomous service robots may lead to conflicts due to mismatches between human expectations and robot behaviour. This interactive online study (N = 335) investigated human-robot interactions at an elevator, focusing on the effect of communication and behavioural expectations on participants' acceptance and compliance. Participants evaluated a humanoid delivery robot primed as either submissive or assertive. The robot either matched or violated these expectations by using a command or appeal to ask for priority and then entering either first or waiting for the next ride. The results highlight that robots are less accepted if they violate expectations by entering first or using a command. Interactions were more effective if participants expected an assertive robot which then asked politely for priority and entered first. The findings emphasize the importance of power expectations in human-robot conflicts for the robot's evaluation and effectiveness in everyday situations.

This thesis explores, through a mixed-methods approach, what happens when companion robots are deployed in care homes for older adults by looking at different perspectives from key stakeholders. Nine studies are presented with decision makers in municipalities, care staff and older adults, as participants, and the studies have primarily been carried out in the field in care homes and activity centres, where both qualitative (e.g., observations and workshops) and quantitative data (surveys) have been collected.  

The thesis shows that companion robots seem to be here to stay and that they can contribute to a higher quality of life for some older adults. It further presents some challenges with a certain discrepancy between what decision makers want and what staff might be able to facilitate. For future research and use of companion robots, it is key to evaluate each robot model and potential use case separately and develop clear routines for how they should be used, and most importantly, let all stakeholders be part of the process. The knowledge contribution is the holistic view of how different actors affect each other when emerging robot technology is introduced in a care environment.    

Understanding how people's attitudes and trust toward robots change over time based on prolonged interaction is crucial for their successful integration into real-world environments. However, long-term deployments of robotic systems in natural settings are relatively rare. In this work, we administered a survey to bicyclists (N = 94) who shared the road with automated shuttle buses for up to three years on their daily commute. The results of the survey show that cyclists with more interaction experience reported more negative attitudes toward the presence of the buses in the shared space, referring to their low usage as one of the main reasons. This finding underscores the significance of perceived usefulness in shaping long-term acceptance of automated services.

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 technologies - and 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.

Aiming for "humanlike" or "natural" interactions can make social robots and their limitations more difficult to understand.

Method: Thirty-one participants drove in simulated rural (high-speed) and urban (low-speed) scenarios. Five hazards were created for each scenario and the participants drove each scenario three times with different latencies (baseline, +100 ms, and +200 ms). The latency condition was masked for the participants. The hazards were designed with the intention of creating challenging traffic situations. For example, in hazard one (H1) a car parked next to the road activates their turn indicators and then cuts into the participant's lane close in front of the ego vehicle, forcing the participant to either brake or veer. Latency, type of hazard, and scenarios (high- and low-speed) were all within participants’ variables.

Objective simulator data collected included variables such as reaction time, post-encroachment time, speed variation, distance to hazard, collisions, etc. Subjective data was gathered through questionnaires between each of the balanced latency conditions to assess trust, perceived control, realism of scenarios, and workload etc. After the completed drive, participants were asked to rate in which order they believed they had been subjected to the different latencies. The participants were divided into two groups, experienced drivers and experienced gamers.

Conclusion: There seems to be a certain level of adaptivity among the participants, although they were not told that the latency varied between scenarios, and they could also not guess in which order they drove with the different conditions. In some situations, they drove with larger safety margins, especially experienced gamers in the high-speed scenario. Moreover, the subjective ratings show that participants felt less in control of the vehicle at higher latencies without being able to pinpoint what it was that affected their driving.

Introduction: Although self-driving vehicle technology has been developing rapidly in recent years, there are still many challenges left before full autonomy can possibly be achieved. Remote operation could facilitate the development of autonomous vehicles in a safe and efficient manner by putting a human in the loop without the need for the human to be physically present in the vehicle. In the current experiment, three aspects of remote driving have been investigated in a driving simulator to evaluate the effect of i) latency, ii) type of task to perform, and iii) speed on a number of performance measures. Method: Thirty-one participants drove in simulated rural (high-speed) and urban (low-speed) scenarios. Five hazards were created for each scenario and the participants drove each scenario three times with different latencies (baseline, +100 ms, and +200 ms). The latency condition was masked for the participants. The hazards were designed with the intention of creating challenging traffic situations. For example, in hazard one (H1) a car parked next to the road activates their turn indicators and then cuts into the participant's lane close in front of the ego vehicle, forcing the participant to either brake or veer. Latency, type of hazard, and scenarios (high- and low-speed) were all within participants' variables. Objective simulator data collected included variables such as reaction time, post-encroachment time, speed variation, distance to hazard, collisions, etc. Subjective data was gathered through questionnaires between each of the balanced latency conditions to assess trust, perceived control, realism of scenarios, and workload etc. After the completed drive, participants were asked to rate in which order they believed they had been subjected to the different latencies. The participants were divided into two groups, experienced drivers and experienced gamers. Result: The results of the simulator study show that for some of the hazards, but not all, there were significant differences in the latency conditions and there were interaction effects between participant groups and environment/speed. For example, in H1 the effect on the reaction time was significantly larger than the added latency. Overall, the experienced gamers drove with larger safety margins although they had not been told that the latency was varied. Speed, latency, and group characteristics were interacting in significant ways and affected performance measures. The subjective ratings show that participants experienced less control of the vehicle during higher latency conditions, even though they were not told in which order they had been subjected to the latency conditions. The separate tasks to perform were affected differently by the independent measures. The number of collisions was not affected by latency. Conclusion: There seems to be a certain level of adaptivity among the participants, although they were not told that the latency varied between scenarios, and they could also not guess in which order they drove with the different conditions. In some situations, they drove with larger safety margins, especially experienced gamers in the high-speed scenario. Moreover, the subjective ratings show that participants felt less in control of the vehicle at higher latencies without being able to pinpoint what it was that affected their driving.

Robots in our society are commonly perceived as subordinate servants with a lower social status than humans. This often leads to humans prioritizing themselves during conflict situations. This becomes problematic when robots start to directly represent humans as proxies if people do not think of the human operator behind them. This could be considered a cognitive bias of human representation in HRI. To explore the extent of this problem, we conducted a user study featuring several conflict situations. Participants granted more priority to the robot when the human representation was visible. This paper explores the societal consequences and emerging inequities such as potentially deprioritizing humans by deprioritizing a robot in certain situations. Possible strategies to address potential negative consequences are discussed on a design level while acknowledging that a societal change in how we perceive and treat robots that represent humans might be necessary.

Prototyping methods are commonly employed iteratively throughout the design and product development, typically ranging from early low-fidelity to later high-fidelity prototypes. We present a case study focusing on prototyping a receptionist agent migrating between three platforms (a monitor on the wall, a mobile phone, and a physical robot). More specifically, we compare virtual reality (VR) and physical (real world) prototyping methods. The two methods are compared in terms of fidelity and usability. The breadth of features, the degree of functionality, and the interactivity were similar. However, the aesthetic refinement differed. The VR prototyping method also had much higher prerequisites in terms of equipment and skills, and the learning curve for the designer was steep. Both methods were equally efficient in user testing, but the VR method revealed more usability issues in the efficiency category, while the physical space method revealed more issues in the effectiveness category.

Understanding how people attribute behavior to underlying mental states is crucial to the study of human social interactions. Previous research has established that people rely on attributing mental states to interpret and interact with artificial agents. For example, a pedestrian encountering a driverless vehicle at a crosswalk might view the vehicle as knowing (or not) that there is a pedestrian in front of it. Nevertheless, little attention has been devoted to investigating people's attributions of higher-order mental states, i.e., mental states that are about the mental states of others (e.g., the vehicle's beliefs about the pedestrian's intentions). Addressing this research gap, the present study conducted a survey to explore people's attributions and preferences concerning higher-order mental states in three types of artificial agents (AI chatbot, virtual assistant, and self-driving car), alongside two human agents (participants themselves, referred to as you, and five-year-old child). The survey revealed that: (1) artificial agents, in contrast to humans, may be perceived as more likely to have higher-order mental states than first-order mental states, depending on the purpose or function of the agent; (2) people may prefer some artificial agents to have mental states of a particular order (but not others); (3) attributions and preferences regarding mental states in artificial agents do not always match. The study also contributes insights regarding the methodological challenge of constructing a survey that effectively captures participants' higher-order attributions while minimizing excessive cognitive demands. We posit that human-agent interaction research and design stand to benefit from further exploration of people's attributions and preferences regarding higher-order mental states in artificial agents.

The rapid advancement of autonomous vehicle (AV) technology has opened up new possibilities and challenges in the domain of human-agent interaction. As AVs become increasingly prevalent on our roads, it is crucial to understand how humans perceive and interact with these intelligent systems. This workshop aims to bring together researchers and practitioners to explore the perception of cars as social agents. We explore the shift in user perception and the implications for interactions between autonomous vehicles, human drivers, and vulnerable road users (pedestrians, cyclists, etc.). Additionally, we investigate the communication of goals and intentions between cars and humans, as well as issues related to mixed agency, stakeholder perspectives, in-vehicle avatars, and human-vehicle power dynamics. The workshop aims to uncover the benefits, risks, and design principles associated with this emerging paradigm.

As automated shuttle buses gradually become a part of urban traffic solutions, their interactions with vulnerable road users need careful consideration. However, the cyclists' perspectives on autonomous shuttle buses have not been explored extensively. This research addresses this gap by surveying 50 cyclists who regularly encounter automated shuttle buses. The results show that, in general, cyclists exhibit a high level of trust in the safety of the buses. Nevertheless, approximately one-third of the cyclists expressed disapproval as the buses tend to drive on the bicycle lane, leading them to wish for infrastructural solutions to avoid forcing cyclists to divert to pedestrian walkways. Identifying potential conflicts like these is vital for the development of effective and acceptable human-agent interactions in road traffic environments.

In this commentary we would like to question (a) Clark and Fischer's characterization of the “social artifact puzzle” – which we consider less puzzling than the authors, and (b) their account of social robots as depictions involving three physical scenes – which to us seems unnecessarily complex. We contrast the authors' model with a more parsimonious account based on attributions.

Safe interaction with automated vehicles requires that human road users understand the differences between the capabilities and limitations of human drivers and their artificial counterparts. Here we explore how people judge what self-driving cars versus human drivers can perceive by engaging online study participants in visual perspective taking toward a car pictured in various traffic scenes. The results indicate that people do not expect self-driving cars to differ significantly from human drivers in their capability to perceive objects in the environment. This finding is important because unmet expectations can result in detrimental interaction outcomes, such as traffic accidents. The extent to which people are able to calibrate their expectations remains an open question for future research.

Driving is a highly social activity with various interactions between the different actors involved in traffic. Autonomous vehicles will pose several challenges to the social fabric of traffic and, as most new technology, will lead to changes in human behavior. This will surely hold for interactions between autonomous vehicles and actors \emph{outside} the vehicle. However and importantly, autonomous vehicles will also alter existing and introduce new social situations and interactions for those \emph{inside} the vehicle, which appears to be an under-researched topic. This paper will focus on the passengers of autonomous vehicles. We will discuss some of the implications and expected changes in the relationship between a car and those inside it, and highlight some of the open issues of being enclosed and driven by a highly complex, largely black-box AI system on wheels.

Safe and efficient interaction with autonomous road vehicles requires that human road users, including drivers, cyclists, and pedestrians, understand differences between the capabilities and limitations of self-driving vehicles and those of human drivers. In this study, we explore how people judge the ability of self-driving cars versus human drivers to keep track of out-of-sight objects by engaging online study participants in cognitive perspective taking toward a car in an animated traffic scene. The results indicate that people may expect self-driving cars to have similar object permanence capability as human drivers. This finding is important because unmet expectations on autonomous road vehicles can result in undesirable interaction outcomes, such as traffic accidents.

A migrating receptionist is an intelligent virtual agent (IVA) designed to transfer from one embodiment to another. The embodiment can range from a virtual human-like form to a physical robot. We present a study of the prototyping and evaluation of a migrating virtual receptionist interface. One prototype was made in virtual reality (VR) and one in physical space. The study looks at how to design a migrating virtual receptionist for a department of computer science. The virtual receptionist was designed to migrate from a screen by the entrance, to a mobile phone used while walking, and then to a robot in the coffee area. The evaluation was conducted with 23 participants who interacted with the virtual receptionist using both prototypes. Data was collected with the System Usability Scale (SUS) and Microsoft Reaction Cards. Although the physical space and the virtual space were different, the results of the evaluation are similar. The SUS mean was 83.9 for the physical space and 78.3 for the virtual space. The reaction cards also showed more positive than negative reactions for both prototypes.

Much research in robotic artificial intelligence (AI) and Artificial Life has focused on autonomous agents as an embodied and situated approach to AI. Such systems are commonly viewed as overcoming many of the philosophical problems associated with traditional computationalist AI and cognitive science, such as the grounding problem (Harnad) or the lack of intentionality (Searle), because they have the physical and sensorimotor grounding that traditional AI was argued to lack. Robot lawn mowers and self-driving cars, for example, more or less reliably avoid obstacles, approach charging stations, and so on—and therefore might be considered to have some form of artificial intentionality or intentional directedness. It should be noted, though, that the fact that robots share physical environments with people does not necessarily mean that they are situated in the same perceptual and social world as humans. For people encountering socially interactive systems, such as social robots or automated vehicles, this poses the nontrivial challenge to interpret them as intentional agents to understand and anticipate their behavior but also to keep in mind that the intentionality of artificial bodies is fundamentally different from their natural counterparts. This requires, on one hand, a “suspension of disbelief ” but, on the other hand, also a capacity for the “suspension of belief.” This dual nature of (attributed) artificial intentionality has been addressed only rather superficially in embodied AI and social robotics research. It is therefore argued that Bourgine and Varela’s notion of Artificial Life as the practice of autonomous systems needs to be complemented with a practice of socially interactive autonomous systems, guided by a better understanding of the differences between artificial and biological bodies and their implications in the context of social interactions between people and technology.

Autonomous vehicles that operate on public roads need to be predictable to others, including vulnerable road users. In this study, we asked participants to take the perspective of videotaped pedestrians and cyclists crossing paths with an automated shuttle bus, and to (1) judge whether the bus would stop safely in front of them and (2) report whether the bus's actual stopping behavior accorded with their expectations. The results show that participants expected the bus to brake safely in approximately two thirds of the human-vehicle interactions, more so to pedestrians than cyclists, and that they tended to underestimate rather than overestimate the bus's capability to yield in ways that they considered as safe. These findings have implications for the design and implementation of automated shuttle bus services.

Autonomous vehicles that operate on public roads need to be predictable to others, including vulnerable road users. In this study, we asked participants to take the perspective of videotaped pedestrians and cyclists crossing paths with an automated shuttle bus, and to (1) judge whether the bus would stop safely in front of them and (2) report whether the bus's actual stopping behavior accorded with their expectations. The results show that participants expected the bus to brake safely in approximately two thirds of the human-vehicle interactions, more so to pedestrians than cyclists, and that they tended to underestimate rather than overestimate the bus's capability to yield in ways that they considered as safe. These findings have implications for the design and implementation of automated shuttle bus services.

The overall aim of the school subject technology is to develop pupils’ understanding of technological solutions in everyday life. A starting point for this study is that it is important for teachers in technology to have knowledge of pupils’ prior conceptions of the subject content since these can both support and hinder their learning. In a previous study we found that when pupils (age 7) talk about digital technology and programming, they often refer to out-of-school experiences such as films, television programmes and books. Typically, their descriptions include robots with some form of intelligence. Hence, it seems like children’s culture may have an impact on the conceptions they bring to the technology classroom. In light of this, it is vital that technology teachers have knowledge about how robots and artificial intelligence (AI) are portrayed in children’s culture, and how pupils perceive these portrayals. However, knowledge about these aspects of technology in children’s culture is limited.The purpose of this study is to investigate how artifacts with artificial intelligence are portrayed in television programmes and literature aimed at children. This study is the first step in a larger study aiming to examine younger pupils’ conceptions and ideas about artificial intelligence. A novice conception of artificial intelligence can be described as an understanding of what a programmed device may, or may not, “understand” in relation to a human, which includes discerning th edifferences between the artificial and the human mind. Consequently, as a theoretical framework for investigating how artificial intelligence is portrayed in children’s culture, the concepts of Theoryof Mind (ToM) and Theory of Artificial Mind (ToAM), are used. The empirical material presented in this paper, i.e. four children’s books and a popular children’s television programme, was analysed using a qualitative thematic analysis. The results show that the portrayal of AI is ambiguous. The structure and function of the robot has elements of both human and machine, and the view of the human fictional characters of the robot is sometimes that of a machine, sometimes of a human. In addition, the whole empirical material includes portrayals of AI as a threat as well as a saviour. As regards implications, there is a risk that without real-life experiences of robots, the representations children’s books and other media convey can lead to ambivalent feelings towards real robots.

In recent years, robots have been prevalent in almost all domains. One of the most common applications of social robotics is for education with children. This dissertation addresses the integration of creativity-related education in child-robot interactions. Creativity is a required skill in the 21st century. It is regarded by many researchers as an essential survival skill. It has been established that current educational methods limit children's freedom of expression and therefore, negatively impact their creative abilities. To date, a few research attempts have focused on developing social child-robot interactions to foster children's creativity. 

In this work, methods were investigated to boost children's creativity skills through social interactions with a robot in a storytelling context. To define and evaluate creativity, standard four creativity measures were used throughout the thesis: fluency, flexibility, elaboration and originality. 

First, a social activity was developed to be performed between a social robot and a child. The activity comprises of two games: an interactive priming game and a storytelling game. The activity has been used throughout the thesis to evaluate implemented algorithms and methods. Second, 3 field studies were conducted with 210 school-aged children (5-10 years old). In these studies, the developed activity was used and notions of emotional alignment and creativity alignment between a child and a social robot were examined. In the context of this work, the concept of behavioral alignment refers to the synchronisation between the robot and the child that results in the child mirroring the robot. Emotional alignment occurs when a child mirrors the robot's emotions. Whereas, creativity alignment results in the child behaving creatively as an effect of interacting with a creative robot. Through the conducted studies, the effects of the various types of child-robot behavioral alignment on children's emotional states, engagement with the robot and children's creativity skills were investigated. Third, a computational model that enables a conversational agent to collaboratively interact with a child in a storytelling activity in a creative manner was produced. The computational model was implemented to be used in an integrated manner with the software interface of the storytelling game. The data collected in the first two studies was used to train the computational model that was assessed through the third and last study.

The findings highlight the effectiveness of social robots in promoting children's creativity skills. They emphasize the potential of the developed educational application (storytelling game interface + computational model) in improving children's creative abilities. This work enriches the literature with new insights on developing robot's behaviors that benefit children's creative processes and therefore, is significant to the child-robot interaction (cHRI) community.

Behavioral variability can be used to make robots more human-like, but we propose that it may be wiser to make them less so.

The topic of mental state attribution to robots has been approached by researchers from a variety of disciplines, including psychology, neuroscience, computer science, and philosophy. As a consequence, the empirical studies that have been conducted so far exhibit considerable diversity in terms of how the phenomenon is described and how it is approached from a theoretical and methodological standpoint. This literature review addresses the need for a shared scientific understanding of mental state attribution to robots by systematically and comprehensively collating conceptions, methods, and findings from 155 empirical studies across multiple disciplines. The findings of the review include that: (1) the terminology used to describe mental state attribution to robots is diverse but largely homogenous in usage; (2) the tendency to attribute mental states to robots is determined by factors such as the age and motivation of the human as well as the behavior, appearance, and identity of the robot; (3) there is a computer < robot < human pattern in the tendency to attribute mental states that appears to be moderated by the presence of socially interactive behavior; (4) there are conflicting findings in the empirical literature that stem from different sources of evidence, including self-report and non-verbal behavioral or neurological data. The review contributes toward more cumulative research on the topic and opens up for a transdisciplinary discussion about the nature of the phenomenon and what types of research methods are appropriate for investigation.

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.

A definition of information fusion (IF) as a field of research can benefit researchers within the field, who may use such a definition when motivating their own work and evaluating the contributions of others. Moreover, it can enable researchers and practitioners outside the field to more easily relate their own work to the field and more easily understand the scope of IF techniques and methods. Based on strengths and weaknesses of existing definitions, a definition is proposed that is argued to effectively fulfill the requirements that can be put on a definition of IF as a field of research. Although the proposed definition aims to be precise, it does not fully capture the richness and versatility of the IF field. To address that limitation, we highlight some topics to explore the scope of IF, covering the systems perspective of IF and its relation to ma-chine learning, optimization, robot behavior, opinion aggregation, and databases.

A definition of information fusion (IF) as a field of research can benefit researcherswithin the field, who may use such a definition when motivating their own work and evaluatingthe contributions of others. Moreover, it can enable researchers and practitioners outside thefield to more easily relate their own work to the field and more easily understand the scope of IFtechniques and methods. Based on strengths and weaknesses of existing definitions, a definitionis proposed that is argued to effectively fulfill the requirements that can be put on a definitionof IF as a field of research. Although the proposed definition aims to be precise, it does not fullycapture the richness and versatility of the IF field. To address that limitation, we highlight sometopics to explore the scope of IF, covering the systems perspective of IF and its relation to ma-chine learning, optimization, robot behavior, opinion aggregation, and databases.

Older adults with late-life depression often suffer from cognitive symptoms, such as dementia. This patient group is not prioritised for psychotherapy and therefore often medicated with antidepressants. However, in the last 20-years, the evidence base for psychotherapy has increased and one promising area is technology-based psychotherapy. Investigations of the possibilities in this area are also motivated by the Covid-19 pandemic, where many older adults are isolated, which makes it impossible for them to meet with a therapist. Therefore, we have developed a Wizard of Oz system allowing a human therapist to control a humanoid robot through a graphical user interface, including natural speech for natural conversations, which enables the robot to be stationed in, for example, a care home. For future research, we will conduct user-centered studies with both therapists and older adults to further develop the system.

Emotions serve important regulatory roles in social interaction. Although recognition, modeling, and expression of emotion have been extensively researched in human-robot interaction and related fields, the role of human emotion in perceptions of and interactions with robots has so far received considerably less attention. We here report inconclusive results from a pilot study employing an affect induction procedure to investigate the effect of peoples emotional state on their perceptions of human-likeness and mind in robots, as well as attitudes toward robots. We propose a new study design based on the findings from this study.

From many perspectives there is a need to understand more of how capacity, signal systems, anddriving behavior interact, and naturally since ERTMS is in its early stage, some areas are stillunexplored. The number of retrofitted ERTMS lines and new designed ERTMS lines are increasing inEurope, which implies extra focus on the capacity optimization in the network planning process.Many of these signaling projects involves going from traditional national lineside signaling to ERTMS.One of the parameters in capacity assessments that is relatively unexplored is the impact of the traindriver. One important knowledge gap to fill, is how simulation parameters should be adapted formore realistic capacity planning, based on driving behavior. ERTMS support new possibilities ofsignaling with its design of speed profiles, but what consequences does the choices have? It is animportant topic since ERTMS is a paradigm shift in train driving, and driveability has an impact onsafety, energy-consumption, and capacity. The purpose of this PhD project is adding knowledge tothis field. The work has been carried out incrementally, beginning with investigation of drivingbehavior with Swedish traditional lineside signaling (ATC), and with this new information proceedingto the area of ERTMS. The continuation of this project aims at investigating train driving behavior forERTMS with a proposed new measuring method based on the standardized ETCS radio signaling. Thisnew method offers a broad spectrum of train driving analysis for ERTMS in a new efficient way.Lastly, in the perspective of ERTMS lines have been evaluated as more challenging to drive, asystematic literature review was conducted to understand ERTMS driveability and how driveabilitycan be assessed. In the overall picture the impact on driveability originates from both technological,organizational, and train driver aspects. Analyzing the research objectives in this area aims atcontributing to a more effective and beneficial network planning process.

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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 peoples 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.

Social robots are robots that are intended for social interaction with people. Because of the societal benefits that they are expected to bring, social robots are likely to become more common. Notably, social robots may be able to perform tasks that require social skills, such as communicating efficiently, coordinating actions, managing relationships, and building trust and rapport. However, robotic systems currently lack most of the technological preconditions for interacting socially. This means that until the necessary technology is developed, humans will have to do most of the work coordinating social interactions with robots. However, social robots are a phenomenon that might also challenge the human ability to interact socially. In particular, the actions of social robots may be less predictable to the ordinary people who will interact with them than the comparable actions of humans. In anticipating the actions of other people, we commonly employ folk-psychological assumptions about what others are likely to believe, want, and intend to do, given the situation that they are in. Folk psychology allows us to make instantaneous, unconscious judgments about the likely actions of others around us, and therefore, to interact socially. However, the application of folk psychology will be challenged in the context of social interaction with robots because of significant differences between humans and robots.

This thesis addresses the scope and limits of people's ability to interact socially with robots by treating them as intentional agents, i.e., agents whose behavior is most appropriately predicted by attributing it to underlying intentional states, such as beliefs and desires. The thesis provides an analysis of the problem(s) of attributing behavior-congruent intentional states to robots, with a particular focus on the perceptual belief problem, i.e., the problem of understanding what robots know (and do not know) about objects and events in the environment based on their perception. The thesis presents evidence that people's understanding of robots as intentional agents is important to their ability to interact socially with them but that it may also be significantly limited by (1) the extendability of the rich folk-psychological understanding that people have gained from sociocultural experiences with humans and other social animals to interactions with robots, and (2) the integrability of new experiences with robots into a usable and reasonable accurate folk psychological understanding of them. Studying the formation and application of folk psychology in interactions with robots should therefore be a central undertaking in social robotics research.

Evaluations of social robots for older adults in care home environments during the past 20 years have shown mostly positive results. However, many of these studies have been short-term and with few participants, as well as limited to few countries. Recent evidence, however, indicates that social robots might not work in all settings or for everyone. Therefore, we conducted a participatory workshop with key stakeholders as an attempt to begin to disentangle the many interrelated factors behind a successful implementation. The result showed similarities in preferred embodiment and morphology, differences in behavioural complexity and task performance, as well as a maybe surprising lack of interest in emotional support. It further showed that older adults living in care homes prior—to meeting social robots—showed relatively little interest in these robots. Based on these observations, we formulate future research directions.

Evaluations of social robots for older adults in care home environments during the past 20 years have shown mostly positive results. However, many of these studies have been short-term and with few participants, as well as limited to few countries. Recent evidence, however, indicates that social robots might not work in all settings or for everyone. Therefore, we conducted a participatory workshop with key stakeholders as an attempt to begin to disentangle the many interrelated factors behind a successful implementation. The result showed similarities in preferred embodiment and morphology, differences in behavioural complexity and task performance, as well as a maybe surprising lack of interest in emotional support. It further showed that older adults living in care homes prior to meeting social robots showed relatively little interest in these robots. Based on these observations, we formulate future research directions.

Future Unmanned Aerial Vehicles (UAVs) are projected to fly and operate in swarms. The swarm metaphor makes explicit and implicit mappings regarding system architecture and human interaction to aspects of natural systems, such as bee societies. Compared to the metaphor of a team, swarming agents as individuals are less capable, more expendable, and more limited in terms of communication and coordination. Given their different features and limitations, the two metaphors could be useful in different scenarios. We also discuss a choir metaphor and illustrate how it can give rise to different design concepts. We conclude that designers and engineers should be mindful of the metaphors they use because they influence—and limit—how to think about and design for multi-UAV systems.

Future Unmanned Aerial Vehicles (UAVs) are projected to fly and operate in swarms. The swarm metaphor makes explicit and implicit mappings regarding system architecture and human interaction to aspects of natural systems, such as bee societies. Compared to the metaphor of a team, swarming agents as individuals are less capable, more expendable, and more limited in terms of communication and coordination. Given their different features and limitations, the two metaphors could be useful in different scenarios. We also discuss a choir metaphor and illustrate how it can give rise to different design concepts. We conclude that designers and engineers should be mindful of the metaphors they use because they influence—and limit—how to think about and design for multi-UAV systems. © 2021, The Author(s)

The explainability of robotic systems depends on people’s ability to reliably attribute perceptual beliefs to robots, i.e., what robots know (or believe) about objects and events in the world based on their perception. However, the perceptual systems of robots are not necessarily well understood by the majority of people interacting with them. In this article, we explain why this is a significant, difficult, and unique problem in social robotics. The inability to judge what a robot knows (and does not know) about the physical environment it shares with people gives rise to a host of communicative and interactive issues, including difficulties to communicate about objects or adapt to events in the environment. The challenge faced by social robotics researchers or designers who want to facilitate appropriate attributions of perceptual beliefs to robots is to shape human–robot interactions so that people understand what robots know about objects and events in the environment. To meet this challenge, we argue, it is necessary to advance our knowledge of when and why people form incorrect or inadequate mental models of robots’ perceptual and cognitive mechanisms. We outline a general approach to studying this empirically and discuss potential solutions to the problem.

Studies of mental state attribution to robots usually rely on verbal measures. However, verbal measures are sensitive to peoples rationalizations, and the outcomes of such measures are not always reflected in a persons behavior. In light of these limitations, we present the first steps toward developing an alternative, non-verbal measure of belief attribution to robots. We report preliminary findings from a comparative study indicating that the two types of measures (verbal vs. non-verbal) are not always consistent. Notably, the divergence between the two measures was larger when the task of inferring the robots belief was more difficult.

In many types of human-robot interactions, people must track the beliefs of robots based on uncertain estimates of robots perceptual and cognitive capabilities. Did the robot see what happened and did it understand what it saw? In this paper, we present preliminary experimental evidence that people estimating what a humanoid robot knows or believes about the environment anthropocentrically assume it to have human-like perceptual and cognitive capabilities. However, our results also suggest that people are able to adjust their incorrect assumptions based on observations of the robot.

Recent developments in robotics are potentially changing the nature of service, and research in human-robot interaction has previously shown that humanoid robots could possibly work in public spaces. We conducted an ethnographic study with the humanoid robot Pepper at a central train station. The results indicate that people are not yet accustomed to talking to robots, and people seem to expect that the robot does not talk, that it is a queue ticket machine, or that, one should interact with it by using the tablet on the robots chest.

Recent developments in robotics are potentially changing the nature of service, and research in human-robot interaction has previously shown that humanoid robots could possibly work in public spaces. We conducted a mixed-method study with the humanoid robot Pepper at a central train station. The results indicate that people are not yet accustomed to talking to robots, and people seem to expect that the robot does not talk, that it is a queue ticket machine, or that one should interact with it by using the tablet on the robot's chest.

Companion robots, and especially robotic pets, have been argued to have the potential for improving the well-being of elderly people with dementia. Previous research has mainly focused on short-term studies, conducted with relatively expensive robot platforms. With cheaper options on the market, residential homes in Sweden have started to use low-cost off-the-shelf platforms, such as the Joy for All cats and dogs, which have not been the subject of much previous research. We therefore conducted two ethnographic long-term studies of real-world use of the Joy for All robot cat and dog at a care home facility. The care staff report positive outcomes regarding reminiscence and improved well-being, with decreased agitation and increased communication. Furthermore, the robots are perceived to provide companionship and to give patients the feeling of being able to take care of someone. Based on the insights gained in this real-world study of the use of robotic pets in elderly care, we identify a number of research questions and methodological issues for future research.

Keeping track of others perceptual beliefs-what they perceive and know about the current situation-is imperative in many social contexts. In a series of experiments, we set out to investigate peoples ability to keep track of what robots knowor believe about objects and events in the environment. To this end, we subjected 155 experimental participants to an anticipatory-looking false-belief task where they had to reason about a robots perceptual capability in order to predict its behavior. We conclude that (1) it is difficult for people to track the perceptual beliefs of a robot whose perceptual capability potentially differs significantly from human perception, (2) people can gradually "tune in" to the unique perceptual capabilities of a robot over time by observing it interact with the environment, and (3) providing people with verbal information about a robots perceptual capability might not significantly help them predict its behavior.

Swarms of autonomous and coordinating Unmanned Aerial Vehicles (UAVs) are rapidly being developed to enable simultaneous control of multiple UAVs. In the field of Human-Swarm Interaction (HSI), researchers develop and study swarm algorithms and various means of control and evaluate their cognitive and task performance. There is, however, a lack of research describing how UAV swarms will fit into future real-world domain contexts. To remedy this, this paper describes a case study conducted within the community of firefighters, more precisely two Swedish fire departments that regularly deploy UAVs in fire responses. Based on an initial description of how their UAVs are used in a forest firefighting context, participating UAV operators and unit commanders envisioned a scenario that showed how the swarm and its capabilities could be utilized given the constraints and requirements of a forest firefighting mission. Based on this swarm scenario description we developed a swarm interaction model that describes how the operators’ interaction traverses multiple levels ranging from the entire swarm, via subswarms and individual UAVs, to specific sensors and equipment carried by the UAVs. The results suggest that human-in-the-loop simulation studies need to enable interaction across multiple swarm levels as this interaction may exert additional cognitive strain on the human operator.

Peoples mental models of robots affect their predictions of robot behavior in interactions. The present study highlights some of the uncertainties that enter specifically into peoples considerations about the minds and behavior of robots by exploring how people fare in the standard "Sally-Anne" false-belief task from developmental psychology when the protagonist is a robot.

We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of childrens behavior during therapy. This public release of the dataset comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included. We release this data with the hope of supporting further data-driven studies towards improved therapy methods as well as a better understanding of ASD in general.

We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children’s behavior during therapy. This public release of the dataset comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included. We release this data with the hope of supporting further data-driven studies towards improved therapy methods as well as a better understanding of ASD in general.