To be able to learn effectively, robots sometimes will need to select more suitable human teachers. We propose an attribute in human teachers for robots that learn through visual observations, namely human teachers’ awareness of and attention to the robot’s visual capabilities and constraints, and explore how it affects robot learning outcomes. In an in-person experiment involving 72 participants who taught three physical tasks to an iCub humanoid robot, we manipulated teachers’ awareness of the robot’s visual constraints by offering the visual perspective of the robot in one of the experimental conditions. Participants who were able to see the robot’s vision output paid increased attention to ensuring task objects were visible to the robot when providing demonstrations of physical tasks. This emphasis on attention to the robot’s view resulted in better learning outcomes for the robot, as indicated by lower perception error rates and higher learning scores. This study contributes to understanding factors in human teachers that lead to better learning outcomes for robots.

Social robots have great potential in supporting individuals’ physical and mental health/wellbeing. While they have been increasingly evaluated in some domains, such as with children with autism, their evaluation has not been as extensive in other areas. We present a systematic review of domains in which social robots have been evaluated specifically in health/wellbeing contexts. We ask which robots have been evaluated, who the participants were, and how participants interacted with the robots. PRISMA guidelines for systematic reviews were followed. Articles with children as participants, using a purely robotic device, and in languages other than English were excluded. A total of 9,362 peer-reviewed articles (up to February 2021) from ACM DL, IEEE Xplore, Scopus, PubMed, and PsychInfo were identified. After applying the inclusion/exclusion criteria 443 articles were included in the review. The majority of studies were conducted at care centers while studies in hospitals/clinics have seen relatively limited attention. In many cases, the social robots were not programmed for specific health-related tasks, limiting their application. We also discuss robots used in real-world settings and propose a “Personal healthcare journey,” which includes different stages of one’s life which could benefit from a social robot, with the goal of increasing long-term adoption of social robots for supporting health/wellbeing.

Public speaking anxiety is one of the most common subtypes of social anxiety and is a prevalent concern among university students. Many students experience excessive anxiety when giving presentations in front of other people, which can negatively impact their academic performance and overall mental well-being. With limited access to human coaches and interventions, there is a need for innovative technological solutions, including social robots, to extend and enhance mental health support and accessibility. In this article, we first outline a co-design study with five mental health professionals and a participatory design study with six university students, aiming to design a robotic mental well-being coach to help university students manage public speaking anxiety. Afterwards, we detail a user study with 50 university students to evaluate the usability and acceptability of the developed robotic mental well-being coach system. The findings showed that the robotic coach system, which includes the robot and a tablet, received a usability score of 84.05 and had high acceptability among participants who perceived the robot as knowledgeable and competent. Moreover, participants’ self-reported moods significantly improved following the study. Overall, the qualitative and quantitative analyses in this study yield promising results regarding the potential use of robotic coaches to help university students manage their public speaking anxiety.

For social robots to succeed in places such as homes, they must learn new skills from various people and act in a manner desirable to different users. We introduce a novel biologically inspired approach for robot learning through program-level imitation, inspired by the way primates, including humans, understand and perform complex actions. Our approach enables robots to discover the hierarchical structure of tasks by identifying sequential regularities and sub-goals from diverse human demonstrations. To do so, human-provided demonstrations, which can be obtained by a robot through different modalities (such as kinesthetic teaching, behavioural observation, and verbal instruction), are processed by an algorithm that discovers multiple possibilities for arranging observed sub-goals to achieve a final goal. Prior to acting, the available sequences are evaluated based on user-defined criteria, through mental simulation of the task by the robot, to find the optimal sequence of actions. As a proof-of-concept, we implemented our system on an iCub humanoid robot and present here how our method allowed the robot to adapt its action sequences for task execution when starting the task from different states, incorporating user preference for finishing the task as fast as possible. Our envisaged system is meant to accommodate variations in human teaching styles and is expected to help a robot perform tasks with greater flexibility and efficiency. This work contributes by proposing a framework for robots to learn from humans at an abstract level, opening the way to more adaptable and intelligent robotic assistants in everyday tasks.

Past studies have emphasized the importance of adjusting agent personalities for improving users’ acceptance and engagement. However, it is not yet clear how agent personalities can be decided on, as preferences have highly varied in different studies and are task/context dependent. In this proof of concept study, we use Affect Control Theory (ACT) to evaluate perceived affective dimensions of personality (called identities thereafter) of 11 different social robots, and study how this perception affects participants’ interests in interacting with the robots. We ask whether ACT can be used as a novel approach to identify participants’ preferred identities for robots in health/therapy contexts. An online study with 95 participants (a total of 1045 robot ratings) was conducted. Our study supports the use of ACT for understanding users’ preferences for social robot identities measured through robot images: the closer the participants rated their own identity to a robot’s, the more interested they reported to be in using the robot in a health/well-being context. We also report on different factors that influenced rating of social robots as described by the participants, such as robot’s size, animal/human-likeness, and perceived friendliness and complexity. We finally discuss advantages of using ACT as an alternative method, compared to Big 5 dimensions, to assess user and agent/robot identities and to guide personalization.

Public speaking anxiety, one of the most common subtypes of social anxiety, is prevalent among university students and can negatively impact their academic success and mental well-being. Limited access to human coaches and interventions calls for innovative technological solutions, including social robots, to extend and complement mental health support and increase accessibility. This study employs a co-design approach to design a robotic mental well-being coach aimed at assisting university students in managing public speaking anxiety. Collaborative co-design sessions with five mental health professionals were conducted to identify the design-related needs (i.e., robot behaviour and interactions) for developing a robotic coach that can effectively assist students’ public speaking anxiety. In addition, a co-design study involving university students was conducted to gather opinions for further improvements of the robotic coach. Students provided feedback on the developed system and generally found the robot engaging, relaxing, knowledgeable, and beneficial for learning relaxation exercises. The findings provide insights into the development of a robotic coach for supporting university students in managing public speaking anxiety.

Social robots have the potential to serve as coaches for public speaking training. To design successful social robots, it is important to understand the expectations and perceptions of prospective users of such robots. In this paper, we present thematic analyses of comments made by 168 participants in an online study where participants watched videos of agents in the role of a public speaking coach. The study had a between-participant design with three conditions: two conditions with a humanoid social robot in either (1) active listening mode, i.e., using non-verbal backchanneling, or (2) passive listening mode, and (3) a voice assistant agent. The themes identified and discussed can contribute to the development of social robots and other agents as public speaking coaches.

Emotions can provide a natural communication modality to complement the existing multi-modal capabilities of social robots, such as text and speech, in many domains. We conducted three online studies with 112, 223, and 151 participants, respectively, to investigate the benefits of using emotions as a communication modality for Search And Rescue (SAR) robots. In the first experiment, we investigated the feasibility of conveying information related to SAR situations through robots’ emotions, resulting in mappings from SAR situations to emotions. The second study used Affect Control Theory as an alternative method for deriving such mappings. This method is more flexible, e.g., allows for such mappings to be adjusted for different emotion sets and different robots. In the third experiment, we created affective expressions for an appearance-constrained outdoor field research robot using LEDs as an expressive channel. Using these affective expressions in a variety of simulated SAR situations, we evaluated the effect of these expressions on participants’ (in the role rescue workers) situational awareness. Our results and proposed methodologies (a) provide insights on how emotions could help conveying messages in the context of SAR, and (b) show evidence on the effectiveness of adding emotions as a communication modality in a (simulated) SAR communication context.

In this article, we analyze the performance of an agent developed according to a well-accepted appraisal theory of human emotion with respect to how it modulates play in the context of a social dilemma. We ask if the agent will be capable of generating interactions that are considered to be more human-like than machine-like. We conducted an experiment with 117 participants and show how participants rated our agent on dimensions of human-uniqueness (separating humans from animals) and human-nature (separating humans from machines). We show that our appraisal theoretic agent is perceived to be more human-like than the baseline models, by significantly improving both human-nature and human-uniqueness aspects of the intelligent agent. We also show that perception of humanness positively affects enjoyment and cooperation in the social dilemma, and discuss consequences for the task duration recall.

To be successful, robots that can learn new tasks from humans should interact effectively with them while being trained, and humans should be able to trust the robots’ abilities after teaching. Typically, when human learners make mistakes, their teachers tolerate those errors, especially when students exhibit acceptable progress overall. But how do errors and appearance of a trainee robot affect human teachers’ trust while the robot is generally improving in performing a task? First, an online survey with 173 participants investigated perceived severity of robot errors in performing a cooking task. These findings were then used in an interactive online experiment with 138 participants, in which the participants were able to remotely teach their food preparation preferences to trainee robots with two different appearances. Compared with an untidy-looking robot, a tidy-looking robot was rated as more professional, without impacting participants’ trust. Furthermore, while larger errors at the end of iterative training had a greater impact, even a small error could significantly reduce trust in a trainee robot performing the domestic physical task of food preparation, regardless of the robot’s appearance. The present study extends human–robot interaction knowledge about teachers’ perception of trainee robots, particularly when teachers observe them accomplishing domestic physical tasks.

Planning an autonomous vehicle’s (AV) path in a space shared with pedestrians requires reasoning about pedestrians’ future trajectories. A practical pedestrian trajectory prediction algorithm for the use of AVs needs to consider the effect of the vehicle’s interactions with the pedestrians on pedestrians’ future motion behaviours. In this regard, this paper systematically reviews different methods proposed in the literature for modelling pedestrian trajectory prediction in presence of vehicles that can be applied for unstructured environments. This paper also investigates specific considerations for pedestrian-vehicle interaction (compared with pedestrian-pedestrian interaction) and reviews how different variables such as prediction uncertainties and behavioural differences are accounted for in the previously proposed prediction models. PRISMA guidelines were followed. Articles that did not consider vehicle and pedestrian interactions or actual trajectories, and articles that only focused on road crossing were excluded. A total of 1260 unique peer-reviewed articles from ACM Digital Library, IEEE Xplore, and Scopus databases were identified in the search. 64 articles were included in the final review as they met the inclusion and exclusion criteria. An overview of datasets containing trajectory data of both pedestrians and vehicles used by the reviewed papers has been provided. Research gaps and directions for future work, such as having more effective definition of interacting agents in deep learning methods and the need for gathering more datasets of mixed traffic in unstructured environments are discussed.

Mental health problems are on the rise among university students. Many students face overwhelming stress and anxiety when participating in different activities and interacting with peers, which can affect their performance and mental well-being. However, many are unlikely to seek or receive help. Intelligent agents can offer the possibility of delivering health and mental well-being interventions with the aim of extending and complementing mental health interventions and increasing accessibility. To provide efficient interventions for students, it is imperative to identify design elements and functionalities that are most effective for engaging students. In this paper, we conducted an online survey with 85 participants (undergraduate and graduate students) to investigate preferences for using intelligent agents (e.g., conversational agents, social robots, etc.) to support their mental well-being, specifically to deal with feelings of stress and anxiety in social situations that are common in academic contexts. We asked students to complete a questionnaire in order to explore students’ experience of anxiety and their perceptions of different aspects of intelligent agents in the context of managing anxiety. The results provide insights on different social and technical capabilities as well as design elements that need to be considered when developing intelligent agents to help address stress and anxiety among university students.

Social robots have been used in mental health care interventions not only to increase access to mental health treatments, but also to complement the support provided by practitioners. We propose incorporating social robots in conventional treatments for children and adolescents with Social Anxiety Disorder (SAD). Although non-robotic, evidence-based interventions for social anxiety are already available, factors such as embarrassment, and anticipatory anxiety have led to treatment delay and avoidance among this clinical population. To encourage treatment and to further improve treatment outcomes, in this work-in-progress article we propose the incorporation of social robots in conventional treatments for SAD. Social robots offer many advantages, such as adaptability, being non-judgmental, and providing interaction capabilities, which could make them a useful tool in the hands of practitioners working with children and adolescents with SAD. We discuss the different roles that social robots could play in helping children with social anxiety make the most of conventional treatments. We also present preliminary results (68 participants) on adolescents’ preferences for using intelligent agents in promoting mental well-being. We conclude by summarizing the potential benefits and limitations of using social robots in conventional treatments for social anxiety.

When humans make a mistake, they often try to employ some strategies to manage the situation and possibly mitigate the negative effects of the mistake. Robots that operate in the real world will also make errors and therefore might benefit from such recovery strategies. In this work, we studied how different verbal expression strategies of a trainee humanoid robot when committing an error after learning a task influence participants’ intention to use it. We performed a virtual experiment in which the expression modes of the robot were as follows: (1) being silent; (2) verbal expression but ignoring any errors; or (3) verbal expression while mentioning any error by apologizing, as well as acknowledging and justifying the error. To simulate teaching, participants remotely demonstrated their preferences to the robot in a series of food preparation tasks; however, at the very end of the teaching session, the robot made an error (in two of the three experimental conditions). Based on data collected from 176 participants, we observed that, compared to the mode where the robot remained silent, both modes where the robot utilized verbal expression could significantly enhance participants’ intention to use the robot in the future if it made an error in the last practice round. When no error occurred at the end of the practice rounds, a silent robot was preferred and increased participants’ intention to use.

It has been suggested that autonomous vehicles can improve efficiency and safety of the transportation systems. While research in this area often focuses on autonomous vehicles which operate on roads, the deployment of low-speed, autonomous vehicles in unstructured, crowded environments has been studied less well and requires specific considerations regarding their interaction with pedestrians. For making the operation of these vehicles acceptable, their behaviour needs to be perceived as safe by both pedestrians and the passengers riding the vehicle. In this paper we conducted an online survey with 116 participants, to understand people’s preferences with respect to an autonomous golf cart’s behaviour in different interaction scenarios. We measured people’s self-reported perceived safety towards different behaviour of the cart in a variety of scenarios. Results suggested that despite the unstructured nature of the environment, the cart was expected to follow common traffic rules when interacting with a group of pedestrians.

Conversational Agents (CA) have become one of the common user interfaces in many online domains. In this paper, we ask whether users have a preference about the personality of CAs, and whether this preference changes depending on the length and type of the tasks CAs are used for. In an online study (N = 410), we investigated three different CA personalities (introvert, extrovert, and non-personified) in four different tasks with different natures and lengths (teaching, booking, todo, and weather). Most of the participants preferred to interact with a conversational agent (introvert or extrovert) as opposed to a non-personified interface, regardless of their own personality. Results suggested that this preference may be task dependent: when CA’s goal was to provide information, participants preferred an extrovert agent. We did not observe a difference between the preference for introvert and extrovert agents when the task’s goal was to complete an assignment.

When human students practise new skills with a teacher, they often display nonverbal behaviours (e.g., head and limb movements, gaze, etc.) to communicate their level of understanding and expressing their interest in the task. Similarly, a student robot's capability to provide human teachers with social signals to express its internal state might improve learning outcomes. This could also lead to a more successful social interactions between intelligent robots and human teachers. However, to design successful nonverbal communication for a robot, we first need to understand how human teachers interpret such nonverbal cues when watching a trainee robot practising a task. Therefore, in this paper, we study the effects of different gaze behaviours as well as manipulating speed and smoothness of arm movement on human teachers' perception of a robot's (a) confidence, (b) eagerness to learn, and (c) attention to the task. In an online experiment, we asked the 167 participants (as teachers) to rate the behaviours of a trainee robot in the context of learning a physical task. The results suggest that splitting the robot's gaze between the teacher and the task not only affects the perceived attention, but can also make the robot appear to be more eager to learn. Furthermore, perceptions of all three attributes tested were systematically affected by varying parameters of the robot's arm movement trajectory while performing task actions.

It is anticipated that intelligent robots will gain the ability to learn from humans how to perform tasks, and will assist them in many contexts such as with household chores in the near future; therefore, people should have the confidence to trust these robots after teaching them how to do a task. Like most machines, robots may sometimes behave in an erroneous manner and such errors can easily undermine trust in the robots, depending on their severity. Nevertheless, when a robot has been taught a task by humans, we hypothesize that the teachers may ignore small mistakes made by the robot, if it shows significant improvements while practising the task. We first conducted a study with 173 participants in which the perceived severity of different robot errors in a household chore (preparing food) was investigated. We then used the results to create scenarios of different levels of severity and conducted a second study with 138 participants to investigate the impact of error severity on trust. Participants remotely taught their preferences in food preparation tasks to robots. Over several practice rounds, robots’ behaviour improved, but the robots made either (a) no errors, (b) a small, or (c) a big error at the end, depending on the experimental condition. Small errors significantly affected trust and big errors had an even more adverse impact. Trust in the robot was found to be correlated with personality traits of the participants as well as with their disposition to trust other people.

Intelligent assistive robots can enhance the quality of life of people with dementia and their caregivers. They can increase the independence of older adults, reduce tensions between a person with dementia and their caregiver, and increase social engagement. This article provides a review of assistive robots designed for and evaluated by persons with dementia. Assistive robots that only increased mobility or brain-computer interfaces were excluded. Google Scholar, IEEE Digital Library, PubMed, and ACM Digital Library were searched. A final set of 53 articles covering research in 16 different countries are reviewed. Assistive robots are categorized into five different applications and evaluated for their effectiveness, as well as the robots’ social and emotional capabilities. Our findings show that robots used in the context of therapy or for increasing engagement received the most attention in the literature, whereas the robots that assist by providing health guidance or help with an activity of daily living received relatively limited attention. PARO was the most commonly used robot in dementia care studies. The effectiveness of each assistive robot and the outcome of the studies are discussed, and particularly, the social/emotional capabilities of each assistive robot are summarized. Gaps in the research literature are identified and we provide directions for future work.

An experiment is presented to investigate whether there is consensus in mapping emotions to messages/situations in urban search and rescue scenarios, where efficiency and effectiveness of interactions are key to success. We studied mappings between 10 specific messages, presented in two different communication styles, reflecting common situations that might happen during search and rescue missions, and the emotions exhibited by robots in those situations. The data was obtained through a Mechanical Turk study with 78 participants. Our findings support the feasibility of using emotions as an additional communication channel to improve multi-modal human-robot interaction for urban search and rescue robots, and suggests that these mappings are robust, i.e. are not affected by the robot's communication style.

We face delays in a variety of situations. They are either inevitable, e.g., due to system limits, or are intentionally added, e.g., advertisements. In many situations, a visual feedback is provided during the delay to manage expectations. This feedback is usually provided through progress bars, percentages, or countdowns, depending on design limitations such as screen size. In this article, we use 15-second delays and examine (a) how delays affect users’ decision-making and task satisfaction, and (b) how to manipulate time perception to reduce the negative consequences of delays. Experiment 1 ( N =421) shows that faster countdowns increase task satisfaction and lead to more rational decisions in the subsequent task. In Experiment 2, we investigate the effect of countdown speed on delay perception and recall ( N =531). We show that faster countdowns lead to shorter perceived delays, while the delay will be recalled as longer after the task. The opposite is obtained for slower countdowns. We also increased the countdown rate and found a limit for the effect of increased speed. Thus, designers have to trade-off between how delays are perceived at the moment of experience and how they are recalled. We discuss the implications of these findings for user interface design.

This paper presents a systematic review of the literature on the effectiveness of engineering countermeasures at reducing unintentional Red-Light Running (RLR) violations and improving safety at traffic intersections. 26 relevant studies on countdown timers, pavement markings, signal operations, advance warning systems, and in-vehicle warning systems are discussed and their results are summarized. While all countermeasures demonstrated varying levels of effectiveness, in-vehicle warning systems that provided audio and/or visual feedback to drivers were found to be the most promising in lowering RLR rates, with studies showing RLR reduction by 84.3%, collision rate reduction by 37%, lower RLR probability and lower risks of crashes. Limitations of each countermeasure are discussed and research shortcomings are indicated. Further areas of potential advancements are highlighted and refinement of countermeasures are proposed in light of improving their effectiveness in reducing RLR violations and improving intersection safety.

In this paper, we examine the origination and dispersion of neologisms from the perspective of both communities and cognitive modeling. We use the Reddit corpus to identify words that were first used by Reddit communities from 2013 to 2014. We induce a hierarchy on Reddit based on the specificity of the topic. Generally, less specific communities have more users, while more specific communities likely feature closer social ties. We ask whether larger numbers of people or closer social ties are better environments to faster the adoption of new words. We found that the majority of new words are first adopted/created in more general communities; though this account is relativized by the size of the communities. We also examined the pace of dispersion of such words in new communities and discuss how this relates to models of memory by using an ACT-R cognitive model. We discuss some parameterizations of such a model of memory and its implications in the word adoption paradigm. Finally, we show that there is an increasing trend in the number of new words being adopted/created in Reddit communities, even during a relatively short time period.

Uber is a ride-sharing platform that is part of the 'gig-economy,' where the platform supports and coordinates a labor market in which there are a large number of ephemeral, piecemeal jobs. Despite numerous efforts to understand the impacts of these platforms and their algorithms on Uber drivers, how to better serve and support drivers with these platforms remains an open challenge. In this paper, we frame Uber through the lens of Stakeholder Theory to highlight drivers' position in the workplace, which helps inform the design of a more ethical and effective platform. To this end, we analyzed Uber drivers' forum discussions about their lived experiences of working with the Uber platform. We identify and discuss the impact of the stakes that drivers have in relation to both the Uber corporation and their passengers, and look at how these stakes impact both the platform and drivers' practices.

Countdowns and progress bars provide computer users with estimates of remaining wait times. These types of feedback are intended to manage their expectations and allow users to direct attention elsewhere. We suggest that they also moderate user's impatience, which affects decision-making in the subsequent task. In an experiment with 421 participants, impatience in a timing decision task was effectively and systematically manipulated through a countdown, as it affected timing and performance of the user's actions in the task. The effect persisted even after users gained task experience. More rapid countdowns reduced impatience. Post-hoc analysis also showed increased task satisfaction with rising countdown speed and suggested greater task satisfaction with a rapid countdown than with no waiting period at all.