In this paper, we critically examine the current "humanoid hype" in robotics, questioning its alignment with responsible robotics principles. While technical challenges drive internal fascination, the pervasive public image of humanoids demands deeper HRI engagement. We explore how responsible robotics concepts, such as privacy, dignity, and trust, are uniquely challenged or overlooked in the pursuit of anthropomorphic robot forms. By dissecting this hype, and mapping the main findings of the recently-published Roadmap for Responsible Robotics to the humanoids field, we aim to move beyond technical form-factor obsessions to understand the true societal implications and identify potential blind spots for the HRI community.

Semantic-Free Utterances (SFUs, sounds conveying intention without using words) are being increasingly adopted for human-robot interaction (HRI) to communicate affect. In healthcare, where older adults are overrepresented, affective robotics are becoming more common to reduce healthcare professionals' workload. Hence, understanding how older adults perceive and communicate with robots is crucial. Although previous studies have demonstrated a decline in older adults' ability to categorize emotions, it remains unclear how this impacts their comprehension of SFUs used in HRI. This paper investigates the effect of age (and other factors) on listeners' ability to categorize emotions in SFUs designed for HRI. Additionally, we explore listeners' preferences of SFUs for a healthcare robot. Listeners indicated that SFUs' similarities to natural language, the need for a distinction between human and robot, and their expectations of how a hospital robot should sound like, influenced their preferences. Furthermore, we conducted an online emotion categorization task to investigate how age, emotion category, type of SFU (with varying degrees of robot-likeness), listeners' gender, and their experience with robots relate to listeners' ability to categorize emotions. Results confirm that as age increases, there is a decline in emotion categorization performance of SFUs varying by emotion category and type of SFU.

In order to calibrate children’s trust in robots toward appropriate levels in the interaction, reliable trust measures are necessary. Current trust measures are not suitable for measuring children’s trust in a real-time manner. While speech from adult speakers has proven to contain information on their trust, this paper presents a first exploration investigating whether these results hold up in the context of a child-robot interaction. Fifty-eight conversations between children and robots were recorded (N=29), evoking high and low trust moments in the interaction. Correlation tests showed no (strong) predictors of children’s trust in their speech. Limitations and possibilities of how to advance the investigations of an automatic trust measure for child-robot interaction are discussed.

To prepare for a future in which robots are more commonplace, it is important to know what robot behaviors people find socially normative. Previous work suggests that for robots to be accepted by people, the robot should adhere to the prevalent social norms, such as those related to approaching people. However, we do not expect that socially normative approach behaviors for robots can be translated on a one-on-one basis from people to robots, because currently robots have unique and different features to humans, including (but not limited to) wheels, sounds, and shapes. The two studies presented in this article go beyond the state-of-the-art and focus on socially normative approach behaviors for robots. In the first study, we compared people’s responses to violations of personal space done by robots compared to people. In the second study, we explored what features (sound, size, speed) of a robot approaching people have an effect on acceptance. Findings indicate that people are more lenient toward violations of a social norm by a robot as compared to a person. Also, we found that robots can use their unique features to mitigate the negative effects of norm violations by communicating intent.

Unpredictability in robot behaviour can cause difficulties in interacting with robots. However, for social interactions with robots, a degree of unpredictability in robot behaviour may be desirable for facilitating engagement and increasing the attribution of mental states to the robot. To generate a better conceptual understanding of predictability, we looked at two facets of predictability, namely, the ability to predict robot actions and the association of predictability as an attribute of the robot. We carried out a video human-robot interaction study where we manipulated whether participants could either see the cause of a robot’s responsive action or could not see this, because there was no cause, or because we obstructed the visual cues. Our results indicate that when the cause of the robot’s responsive actions was not visible, participants rated the robot as more unpredictable and less competent, compared to when it was visible. The relationship between seeing the cause of the responsive actions and the attribution of competence was partially mediated by the attribution of unpredictability to the robot. We argue that the effects of unpredictability may be mitigated when the robot identifies when a person may not be aware of what the robot wants to respond to and uses additional actions to make its response predictable.

Predictability is important to autistic individuals, and robots have been suggested to meet this need as they can be programmed to be predictable, as well as elicit social interaction. The effectiveness of robot-assisted interventions designed for social skill learning presumably depends on the interplay between robot predictability, engagement in learning, and the individual differences between different autistic children. To better understand this interplay, we report on a study where 24 autistic children participated in a robot-assisted intervention. We manipulated the variance in the robot’s behaviour as a way to vary predictability, and measured the children’s behavioural engagement, visual attention, as well as their individual factors. We found that the children will continue engaging in the activity behaviourally, but may start to pay less visual attention over time to activity-relevant locations when the robot is less predictable. Instead, they increasingly start to look away from the activity. Ultimately, this could negatively influence learning, in particular for tasks with a visual component. Furthermore, severity of autistic features and expressive language ability had a significant impact on behavioural engagement. We consider our results as preliminary evidence that robot predictability is an important factor for keeping children in a state where learning can occur.

Interactive learning technologies, such as robots, increasingly find their way into schools. However, more research is needed to see how children might work with such systems in the future. This paper presents the unsupervised, four month deployment of a Robot-Extended Computer Assisted Learning (RECAL) system with 61 children working in their own classroom. Using automatically collected quantitative data we discuss how their usage patterns and self-regulated learning process developed throughout the study.

A growing number of studies use a "ghost-driver" vehicle driven by a person in a car seat costume to simulate an autonomous vehicle. Using a hidden-driver vehicle in a field study in the Netherlands, Study 1 (N = 130) confirmed that the ghostdriver methodology is valid in Europe and confirmed that European pedestrians change their behavior when encountering a hidden-driver vehicle. As an important extension to past research, we find pedestrian group size is associated with their behavior: groups look longer than singletons when encountering an autonomous vehicle, but look for less time than singletons when encountering a normal vehicle. Study 2 (N = 101) adapted and extended the hidden-driver method to test whether it is believable as online video stimuli and whether car characteristics and participant feelings are related to the beliefs and behavior of pedestrians who see hidden-driver vehicles. As expected, belief rates were lower for hidden-driver vehicles seen in videos compared to in a field study. Importantly, we found noticing no driver was the only significant predictor of belief in car autonomy, which reinforces prior justification for the use of the ghostdriver method. Our contributions are a replication of the hidden-driver method in Europe and comparisons with past US and Mexico data; an extension and evaluation of the ghostdriver method in video form; evidence of the necessity of the hidden driver in creating the illusion of vehicle autonomy; and an extended analysis of how pedestrian group size and feelings relate to pedestrian behavior when encountering a hidden-driver vehicle.

This article presents a study in which we explored the effect of a social robot on the explanatory behavior of children (aged 6--10) while working on an inquiry learning task. In a comparative experiment, we offered children either a baseline Computer Aided Learning (CAL) system or the same CAL system that was supplemented with a social robot to verbally explain their thoughts to. Results indicate that when children made observations in an inquiry learning context, the robot was better able to trigger elaborate explanatory behavior. First, this is shown by a longer duration of explanatory utterances by children who worked with the robot compared to the baseline CAL system. Second, a content analysis of the explanations indicated that children who worked with the robot included more relevant utterances about the task in their explanation. Third, the content analysis shows that children made more logical associations between relevant facets in their explanations when they explained to a robot compared to a baseline CAL system. These results show that social robots that are used as extensions to CAL systems may be beneficial for triggering explanatory behavior in children, which is associated with deeper learning.

Awe is a heightened emotional state of fear and wonder that creates a physiological response resulting in a cascade of hairs standing on end, also known as piloerection or goose-bumps. This latent sense once served an animalian purpose of survival, but now lies dormant and is often not experienced consciously. In fact, 55 percent of the population reports to not feel this sensation that is noted to be healthy. The AWE Goosebumps artifact is an emotion prosthesis that animates the latent sensation of awe for embodiment and externalizes cues for communication. As the sensation is not experienced consciously, the techno fashion invites an opportunity to be a second skin for frisson biofeedback, behavior training, and expression to others as a tool to transform the doldrums of modern day to performative states of wonder.

The so-called fourth industrial revolution and its economic and societal implications are no longer solely an academic concern, but a matter for political as well as public debate. Characterized as the convergence of robotics, AI, autonomous systems and information technology – or cyberphysical systems – the fourth industrial revolution was the focus of the World Economic Forum, at Davos, in 2016[1]. Also in 2016 the US White House initiated a series of public workshops on artificial intelligence (AI) and the creation of an interagency working group, and the European Parliament Committee for Legal Affairs published a draft report with recommendations to the Commission on Civil Law Rules on Robotics.

Robots are expected to enter the everyday lives of people to entertain, educate, or support them. It is therefore important that people can intuitively understand the behavior of robots. Oftentimes, the behavior of people is used as a model because of its familiarity. However, it is as yet unclear what the best approach is to design interaction behaviors for non-humanoid robots. In this article, we explore two different approaches toward designing behavior for a service robot. The first concerns the commonly used approach of copying human behavior as closely as possible to the robot ( human-translated ). The second approach was inspired by product design methods. The design of the robot's behavior was optimized for the robot's interaction capabilities and hardware modalities ( robot-optimized ). To evaluate people's responses to the two behavior sets for a tour guide robot, an online video study ( N = 204) and a two-day in-the-wild study ( N > 600) were performed. Results showed that participants responded slightly more positive to robot-optimized behavior and paid attention to robot-optimized behavior for longer. However, participants remembered more details when the robot showed human-translated behavior. Together, the studies show that it is sometimes better for non-humanoid robots to have robot-optimized behaviors rather than human-translated behaviors.

Researchers and designers of in-vehicle interactions and interfaces currently have to choose between performing evaluation and human factors experiments in laboratory driving simulators or on-road experiments. To enjoy the benefit of customizable course design in controlled experiments with the immediacy and rich sensations of on-road driving, we have developed a new method and tools to enable VR driving simulation in a vehicle as it travels on a road. In this paper, we describe how the cost-effective and flexible implementation of this platform allows for rapid prototyping. A preliminary pilot test (N = 6), centered on an autonomous driving scenario, yields promising results, illustrating proof of concept and indicating that a basic implementation of the system can invoke genuine responses from test participants.

Nanogami is a bioresponsive garment to visualize the importance of the microbiome on collective wellbeing. The microbiome is the group of bacteria, viruses, and cells that live within and on our bodies. This galaxy of particles makes up more than half of the human body and are noted to be responsible for overall health and mood.

Emotions, and emotional expression, have a broad influence on social interactions and are thus a key factor to consider in developing social robots. This study examined the impact of life-like affective facial expressions, in the humanoid robot Zeno, on children’s behaviour and attitudes towards the robot. Results indicate that robot expressions have mixed effects depending on participant gender. Male participants interacting with a responsive facially expressive robot showed a positive affective response and indicated greater liking towards the robot, compared to those interacting with the same robot maintaining a neutral expression. Female participants showed no marked difference across the conditions. We discuss the broader implications of these findings in terms of gender differences in human–robot interaction, noting the importance of the gender appearance in robots (in this case, male) and in relation to advancing the understanding of how interactions with expressive robots could lead to task-appropriate symbiotic relationships.

In this paper, we propose to use deep 3-dimensional convolutional networks (3D CNNs) in order to address the challenge of modelling spectro-temporal dynamics for speech emotion recognition (SER). Compared to a hybrid of Convolutional Neural Network and Long-Short-Term-Memory (CNN-LSTM), our proposed 3D CNNs simultaneously extract short-term and long-term spectral features with a moderate number of parameters. We evaluated our proposed and other state-of-the-art methods in a speaker-independent manner using aggregated corpora that give a large and diverse set of speakers. We found that 1) shallow temporal and moderately deep spectral kernels of a homogeneous architecture are optimal for the task; and 2) our 3D CNNs are more effective for spectro-temporal feature learning compared to other methods. Finally, we visualised the feature space obtained with our proposed method using t-distributed stochastic neighbour embedding (T-SNE) and could observe distinct clusters of emotions.

One of the major reasons behind traffic accidents is misinterpretation among road users. Self-driving vehicles are expected to reduce these accidents, given that they are designed with all road users in mind. Recently, research on the design of vehicle-pedestrian communication has emerged, but to our knowledge, there is no research published that investigates the design of interfaces for intent communication towards child pedestrians. This paper reports the initial steps towards the examination of children's views and understandings about the appearance and intention communication of self-driving vehicles. It adopts a design inclusive methodological approach for the development of a prototype for the communication of two basic intentions: "I am going to stop" and "I am going to proceed". The initial results indicate children's need to be aware about the autonomy of the vehicle and the use of their previous experience with traffic signs for the interpretation of communicative signs of the vehicle.

In this note, we present minimal robot movements for robotic technology for children. Two types of minimal gaze movements were designed: social-gaze movements to communicate social engagement and deictic-gaze movements to communicate task-related referential information. In a two (social-gaze movements vs. none) by two (deictic-gaze movements vs. none) video-based study (n=72), we found that social-gaze movements significantly increased children's perception of animacy and likeability of the robot. Deictic-gaze and social-gaze movements significantly increased children's perception of helpfulness. Our findings show the compelling communicative power of social-gaze movements, and to a lesser extent deictic-gaze movements, and have implications for designers who want to achieve animacy, likeability and helpfulness with simple and easily implementable minimal robot movements. Our work contributes to human-robot interaction research and design by providing a first indication of the potential of minimal robot movements to communicate social engagement and helpful referential information to children.

One of the challenges in Speech Emotion Recognition (SER) "in the wild" is the large mismatch between training and test data (e.g.speakers and tasks).In order to improve the generalisation capabilities of the emotion models, we propose to use Multi-Task Learning (MTL) and use gender and naturalness as auxiliary tasks in deep neural networks.This method was evaluated in within-corpus and various cross-corpus classification experiments that simulate conditions "in the wild".In comparison to Single-Task Learning (STL) based state of the art methods, we found that our MTL method proposed improved performance significantly.Particularly, models using both gender and naturalness achieved more gains than those using either gender or naturalness separately.This benefit was also found in the high-level representations of the feature space, obtained from our method proposed, where discriminative emotional clusters could be observed.

Deep architectures using identity skip-connections have demonstrated groundbreaking performance in the field of image classification. Recently, empirical studies suggested that identity skip-connections enable ensemble-like behaviour of shallow networks, and that depth is not a solo ingredient for their success. Therefore, we examine the potential of identity skip-connections for the task of Speech Emotion Recognition (SER) where moderately deep temporal architectures are often employed. To this end, we propose a novel architecture which regulates unimpeded feature flows and captures long-term dependencies via gate-based skip-connections and a memory mechanism. Our proposed architecture is compared to other state-of-the-art methods of SER and is evaluated on large aggregated corpora recorded in different contexts. Our proposed architecture outperforms the state-of-the-art methods by 9 - 15% and achieves an Unweighted Accuracy of 80.5% in an imbalanced class distribution. In addition, we examine a variant adopting simplified skip-connections of Residual Networks (ResNet) and show that gate-based skip-connections are more effective than simplified skip-connections.

Awe is a powerful, visceral sensation described as a sudden chill or shudder accompanied by goosebumps. People feel awe in the face of extraordinary experiences: the sublimity of nature, the beauty of art and music, the adrenaline rush of fear. Awe is healthy, both physically and mentally. It can be shared by people who are witnessing the same phenomenon, but traditionally it cannot be communicated remotely across time or distance: to feel awe involves real time experience, and explaining the experience that gave rise to it does not always induce the feeling of awe itself. We want to make this sensation something that can be transmitted, and therefore present AWElectric, a wearable interface that can detect awe, enhance it, and create it in another person. Our shared goosebump design embeds inflatable biometric displays in 3D print fabric.The AudioTactile fabric transmits an awe-inducing sound frequency to the partner that physically manifests the tingles, chills, and goosebumps that awe provokes.

Developing systems that motivate people to change their behaviors, such as an exercise application for the smartphone, is challenging. One solution is to implement motivational strategies from existing behavior change theory and tailor these strategies to preferences based on personal characteristics, like personality and gender. We operationalized strategies by collecting representative motivational text messages and aligning the messages to ten theory-based behavior change strategies. We conducted an online survey with 350 participants, where the participants rated 50 of our text messages (each aligned to one of the ten strategies) on how motivating they found them. Results show that differences in personality and gender relate to significant differences in the evaluations of nine out of ten strategies. Eight out of ten strategies were perceived as either more or less motivating in relation to scores on the personality traits Openness, Extraversion, and Agreeableness. Four strategies were perceived as more motivating by men than by women. These findings show that personality and gender influence how motivational strategies are perceived. We conclude that our theory-based behavior change strategies can be more motivating by tailoring them to personality and gender of users of behavior change systems.

Developing motivational technology to support long-term behavior change is a challenge. A solution is to incorporate insights from behavior change theory and design technology to tailor to individual users. We carried out two studies to investigate whether the processes of change, from the Transtheoretical Model, can be effectively represented by motivational text messages. We crowdsourced peer-designed text messages and coded them into categories based on the processes of change. We evaluated whether people perceived messages tailored to their stage of change as motivating. We found that crowdsourcing is an effective method to design motivational messages. Our results indicate that different messages are perceived as motivating depending on the stage of behavior change a person is in. However, while motivational messages related to later stages of change were perceived as motivational for those stages, the motivational messages related to earlier stages of change were not. This indicates that a person's stage of change may not be the (only) key factor that determines behavior change. More individual factors need to be considered to design effective motivational technology.

Many researchers have started to explore natural interaction scenarios for children. No matter if these children are normally developing or have special needs, evaluating Child-Robot Interaction (CRI) is a challenge. To find methods that work well and provide reliable data is difficult, for example because commonly used methods such as questionnaires do not work well particularly with younger children. Previous research has shown that children need support in expressing how they feel about technology. Given this, researchers often choose time-consuming behavioral measures from observations to evaluate CRI. However, these are not necessarily comparable between studies and robots.

We present initial findings from an experiment where we used Semantic Free Utterances - vocalizations and sounds without semantic content - as an alternative to Natural Language in a child-robot collaborative game. We tested (i) if two types of Semantic Free Utterances could be accurately recognized by the children; (ii) what effect the type of Semantic Free Utterances had as part of help-giving behaviors with in situ child-robot interaction. We discuss the potential benefits and pitfalls of Semantic Free Utterances for child-robot interaction.

Social and Affective Robotics is a growing multidisciplinary field encompassing computer science, engineering, psychology, education, and many other disciplines. It explores how social and affective factors influence interactions between humans and robots, and how affect and social signals can be sensed and integrated into the design, implementation, and evaluation of robots. With talks by renowned researchers in this area, Social and Affective Robotics Tutorial will help both new and experienced researchers to identify trends, concepts, methodologies and applications in this field, identified as a technological megatrend driving the fourth industrial revolution.

This review examines recent methodological approaches for the evaluation of child-robot interaction in learning settings. The main aims are to map existing work from a user-centered perspective, to identify possible trends related to evaluation methods for child-robot interaction, and to discuss potential future directions. We present a systematic review of existing studies, which have been thematically organized based on their research objectives. We then examine the evaluation methods that were used in these studies and we propose a conceptual framework based on the one hand on the themes that emerged, namely the social interaction between the child and the robot, the social acceptance, possible emotional interactions, the learning process and the learning outcome, and on the other hand on the corresponding measures. These methods have been considered in relation with the age ranges of the children, because of the relationship of their cognitive level to the choice of a developmentally appropriate evaluation method. We use this framework to highlight current trends and needs for the field and to contextualize the methodological directions for child-robot interaction. Finally, we discuss the challenges and limitations of the current methodological approaches as well as possible future directions for the evaluation methods of child-robot interaction in learning settings.

Since children (5-9 years old) are still developing their emotional and social skills, their social interactional behaviors in small groups might differ from adults' interactional behaviors. In order to develop a robot that is able to support children performing collaborative tasks in small groups, it is necessary to gain a better understanding of how children interact with each other. We were interested in investigating vocal turn-taking patterns as we expect these to reveal relations to collaborative and conflict behaviors, especially with children behaviors as previous literature suggests. To that end, we collected an audiovisual corpus of children performing collaborative tasks together in groups of three. Through automatic turn-taking analyses, our results showed that speaker changes with overlaps are more common than without overlaps and children seemed to show smoother turn-taking patterns, i.e., less frequent and longer lasting speaker changes, during collaborative than conflict behaviors.

One of the ways to gauge your own exercise intensity while running, is to assess your capability of talking while running: if you can still speak comfortably, you are running within the recommended intensity guidelines. This subjective way of estimating one's exercise intensity by talking (i.e. the Talk Test) motivated us to investigate how speech characteristics are affected during running and whether it is possible to develop a more objective way of estimating exercise intensity levels while running through voice analysis. To this end, we developed the Talk & Run Speech database that contains speech recorded from people before, during, and after running. We present our database and show that it is possible to detect exercise intensity below or above the anaerobic threshold in speech during running with a performance of 73.5% and 60.0% (unweighted average recall) for female and male speakers respectively.

Robot users that receive psychological or psy-chotherapeutic support from robots (e.g. robots that motivate users to perform certain tasks) are usually aware of participating in a psychological intervention. The present paper aims to ascertain whether robot users should indeed remain aware, or rather unaware, of participating in such type of interventions. We present an experiment with two conditions. In one condition (direct) the robot made participants aware of being subjected to a psychological intervention, the three good things exercise from positive psychology, whereas in the other condition (indirect) participants were not made aware of the intervention. Our results show that the robot succeeded in improving participants' positive affect in the direct condition but their affect worsened in the indirect condition.

When a mobile robot interacts with a group of people, it has to consider its position and orientation. We introduce a novel study aimed at generating hypotheses on suitable behavior for such social positioning, explicitly focusing on interaction with small groups of users and allowing for the temporal and social dynamics inherent in most interactions. In particular, the interactions we look at are approach, converse and retreat. In this study, groups of three participants and a telepresence robot (controlled remotely by a fourth participant) solved a task together while we collected quantitative and qualitative data, including tracking of positioning/orientation and ratings of the behaviors used. In the data we observed a variety of patterns that can be extrapolated to hypotheses using inductive reasoning. One such pattern/hypothesis is that a (telepresence) robot could pass through a group when retreating, without this affecting how comfortable that retreat is for the group members. Another is that a group will rate the position/orientation of a (telepresence) robot as more comfortable when it is aimed more at the center of that group.

Gestures are important non-verbal signals in human communication. Research with virtual agents and robots has started to add to the scientific knowledge about gestures but many questions with respect to the use of gestures in human-computer interaction are still open. This paper investigates the influence of robot gestures on the users' perceived workload and task performance (i.e. information recall) in a direction-giving task. We conducted a 2 x 2 (robot gestures vs. no robot gestures x easy vs. difficult task) experiment. The results indicate that robot gestures increased user performance and decreased perceived workload in the difficult task but not in the easy task. Thus, robot gestures are a promising means to improve human-robot interaction particularly in challenging tasks.

This video details the development of an intelligent outdoor Guide robot. The main objective is to deploy an innovative robotic guide which is not only able to show information, but to react to the affective states of the users, and to offer location-based services using augmented reality. The scientific challenges concern autonomous outdoor navigation and localization, robust 24/7 operation, affective interaction with visitors through outdoor human and facial feature detection as well as engaging interactive behaviors in an ongoing non-verbal dialogue with the user.

In our previous work we introduced functional noise as a modality for robots to communicate intent [6]. In this follow-up experiment, we replicated the first study with a robot which was taller in order to find out if the same results would apply to a tall vs. a short robot. Our results show a similar trend: a robot using functional noise is perceived more positively compared with a robot that does not.

Research has been carried out on robots approaching one person [1, 3, 4]. However, further research is needed on robots approaching groups of people. In the study reported in this paper, we studied participants who were paired up for a task and assessed their perception and behaviors as they were approached by a robot from various angles. On an individual level, participants liked the frontal approaches, and they disliked being approached from the back. However, we found that the presence of a task-partner influenced participants' comfort with a robot approaching (i.e. when the robot approaches and one is standing behind the task-partner). Apart from the positioning of the individuals, the layout of the room, position of furniture and doors, also seemed to influence their experience. This pilot study was performed with a limited number of participants (N=30). However, the study offers preliminary insights into the factors that influence the choice for a robot approach direction when approaching a pair of people that are focused on a task.

Robots have recently started to leave their safety cages to be used in close vicinity to humans. This also causes changes in the nature of the tasks that robots and humans solve together, i.e., in the degree of structure of the tasks. While traditional, industrial tasks were highly structured, the new tasks often have a low level of structure. We present a user study that compares a highly and a little structured task in a text-based computer game played by human-robot teams. The results suggest that users do not only find robots useful and motivating in highly structured tasks where they depend on their help, but also in little structured tasks that they could solve on their own.

With changes in life expectancy across the world, technologies enhancing well-being of individuals, specifically for older people, are subject to a new stream of research and development. In this paper we present the ACCOMPANY project, a pan-European project which focuses on home companion technologies. The projects aims to progress beyond the state of the art in multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, monitoring persons and chores at home, and technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 and in the context of a smart-home environment utilising a multitude of sensor arrays. The resulting prototype from integrating these developments undergoes multiple formative cycles and a summative evaluation cycle towards identifying acceptable behaviours and roles for the robot for example role as a butler or a trainer. Furthermore, the evaluation activities will use an evaluation grid in order to assess achievement of the identified user requirements, formulated in form of distinct scenarios. Finally, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developments.

People tend to unconsciously attribute personality traits to all kinds of technology including robots. But what personality do they want robots to have? Previous research has found support for two contradicting theories: similarity attraction and complementary attraction. The similarity attraction theory implies that people prefer a robot with a similar personality to their own (e.g., an extroverted person prefers an extroverted robot). According to the complementary attraction theory, people prefer a robot's personality opposite to their own (e.g., extroverted people prefer an introverted robot). In contrast to both theories, we argue that what is considered an appropriate personality for a robot depends on the task context. In a 2×2 between-groups experiment (N=45), we found trends that indicated similarity attraction for extrovert participants when the robot was a tour guide and complementary attraction for introverted participants when the robot was a cleaner. These trends show that preferences for robot personalities may indeed depend on the context of the robot's role and the stereotype perceptions people hold for certain jobs. Robot behaviors likely need to be adapted not in complimentary or similarity to the users' personality but to the users' expectations about what kind of personality and behaviors are consistent with such a task or role.

We evaluated the effects of robot gaze behavior on interactions with multiple users in a museum-like setting. We posit that a robot needs to divide its attention between multiple users and may be able to use its gaze to ‘point’ at objects of interest. A 2 (person-oriented [only looking at participants] vs. object-oriented [also looking at artworks] gaze) × 2 (‘favored’ [looked at more] vs. ‘not favored’ [looked at less] by the robot) mixed factorial design (N=57) study was carried out in a museum-like lab setting where a robot talked about two artworks to groups of three participants. Results indicate that ‘favored’ participants did indeed pay more attention to the robot and the artworks. However, surprisingly they paid more attention when the robot did not look over to the object of interest compared to when it did give this gaze cue. The findings suggest that using an object-oriented gaze as a cue for people to look at an object may not carry across readily from person-to-person to human-robot communication. People had trouble interpreting the cue and were possibly distracted by the robot's movement.

How a robot approaches a person greatly determines the interaction that follows. This is particularly relevant when the person has never interacted with the robot before. In human communication, we exchange a multitude of multimodal signals to communicate our intent while we approach others. However, most robots do not have the capabilities to produce such signals and easily communicate their intent. In this paper we propose to communicate intent when a robot approaches a person through functional noise and approach speed. Both were manipulated in a between-subjects experiment (N=40) either slowly increasing at the start of the approach and slowly decreasing when the robot reached the human or maximized at the start and abruptly stopped at the end of the approach. We analyzed questionnaires and video data from the interaction and found that particularly functional noise that in-/decreased in volume was helpful to communicate the robot's intent but only in congruence with an in-/decreasing velocity.

In this paper we argue for the development of new methodological approaches to create and evaluate robots for elderly-care, which offer support for the psychological determinants of the quality of life of elderly people. Relevant determinants, such as mood, self-efficacy and happiness are discussed in this paper in relation to older people. We offer an overview of previous work on robots offering psychological support and analyse the various methodological challenges in studying the effects of motivational and psycho-therapeutic robots on elderly people's psychological well-being.

When in a human environment, one might expect that a social robot would act according to the social norms people expect of each other. When someone does not adhere to a prevalent social norm, people usually feel threatened and disturbed. Thus, insight is needed into what is perceived as socially normative behavior for robots. We conducted an experiment in which an agent approached a participant in order to determine the effect of personal space invasion. We manipulated the agent-type (human/robot) and the approach speed (slow/fast) of the agent towards the participant. Unexpectedly, our results show that the participants displayed more compensatory behavior in the robot condition than in the human condition. We consider this response toward personal space invasion as indication that people react in a similar way to robots as they do to humans, however with more intensity.

Humans use very sophisticated ways of bodily emotion expression combining facial expressions, sound, gestures and full body posture. Like others, we want to apply these aspects of human communication to ease the interaction between robots and users. In doing so we believe there is a need to consider what abstraction of human social communicative behaviors is appropriate for robots. The study reported in this paper is a pilot study to not offer simulated emotion but to offer an abstracted robot version of emotion expressions and an evaluation to what extent users interpret these robot expressions as the intended emotional states. To this end, we present the mobile, mildly humanized robot Daryl, for which we created six motion sequences that combine human-like, animal-like, and robot-specific social cues. The results of a user study (N=29) show that despite the absence of facial expressions and articulated extremities, subjects' interpretation of Daryl's emotional states were congruent with the abstracted emotion display. These results demonstrate that abstract displays of emotion that combine human-like, animal-like, and robot-specific modalities could in fact be an alternative to complex facial expressions and will feed into ongoing work identifying robot-specific social cues.

FROG (Fun Robotic Outdoor Guide) is a project that aims to develop an outdoor robotic guide that enriches the visitor experience in touristic sites. This paper is a first step toward a guide robot and presents a case study on how to analyze the visitors' experience and examine opportunities for a future robot guide in the sites. We adopted the participatory design method for mapping the visitor experience; the end users of the tourist sites participated actively in finding and discussing their experience of visiting. Results indicated that visitors especially like the structure of the tour and the stories provided, especially interesting little known facts the guide gives. However, they do not like the rushed pace of a guided tour. When exploring the site by themselves, they enjoy the freedom, the time to make pictures and to concentrate on what interests them. Visitors do not like a lack or overload of information or problems with route finding. Not all guide-related factors that influence a visitor's experience positively can be copied one-on-one to a robot guide. And care needs to be taken to identify those aspects of guided tours and guide behaviors that will be effective for robot-guided tours. In this paper we describe the first step towards the realization of an outdoor robotic guide. We evaluate people's experiences of guided tours to inform the design of robot-guided tours. This analysis forms the basis for ongoing research into the development of effective robot behaviors.

In this paper we present first results of an observation study on indoor navigation behaviour of visitors at a large public fair. As an outcome we present a number of requirements for mobile indoor navigation systems that support collaborative destination and path finding tasks.

Environmental monitoring and emergency response projects in urban-industrial areas increasingly rely on efficient collaboration between experts in control rooms and at incident locations, and citizens who live or work in the area. In the video accompanying this abstract we present a system that uses distributed sensor technology, Bayesian decision tools, and advanced map-based interfaces to facilitate collaboration between environmental experts and the public for environmental monitoring and early detection of chemical incidents.

This paper reports ongoing research to inform the design of a social robot to monitor levels of pollutant gasses in the air. Next to licensed environmental agents and immobile chemical sensors, mobile technologies such as robotic agents are needed to collect complaints and smell descriptions from humans in urban industrial areas. These robots will interact with members of the public and ensure responsiveness and accuracy of responses. For robots to be accepted as representative environmental monitoring agents and for people to comply with robot instructions in the case of a calamity, social skills will be important. In this paper we will describe the intelligent environment the environmental robot is part of and discuss preliminary work to understand in what way robot interruptions can be mitigated with help of social robot behaviors.

Mobile services can provide users with information relevant to their current circumstances. Distant services in turn can acquire local information from people in an area of interest. Socially expressive agent behaviour has been suggested as a way to build reciprocal relationships and to increase user response to such requests. This between-subject, Wizard-of-Oz experiment aimed to investigate the potential of such behaviours. 44 participants performed a search task in an urgent context while being interrupted by a mobile agent that both provided and requested information. The socially expressive behaviour shown in this study did not increase compliance to requests; it instead reduced trust in provided information and compliance to warnings. It also negatively impacted the affective experience of users scoring lower on empathy as a personality trait. Inappropriate social expressiveness can have serious consequences; we here elaborate on the reasons for our negative results.

Empathy has great potential in human-robot interaction. However, the challenging nature of assessing the user's emotional state points to the importance of also understanding the effects of empathic behaviours incongruent with users' affective experience. A 3x2 between-subject video-based survey experiment (N=133) was conducted with empathic robot behaviour (empathically accurate, neutral, inaccurate) and valence of the situation (positive, negative) as dimensions. Trust decreased when empathic responses were incongruent with the affective state of the user. However, in the negative valence condition, reported perceived empathic abilities were greater when the robot responded as if the situation were positive.

In this study, we sought to clarify the effects of users' cultural background and cultural context on human-robot team collaboration by investigating attitudes toward and the extent to which people changed their decisions based on the recommendations of a robot collaborator. We report the results of a 2×2 experiment with nationality (Chinese vs. US) and communication style (implicit vs. explicit) as dimensions. The results confirm expectations that when robots behave in more culturally normative ways, subjects are more likely to heed their recommendations. Specifically, subjects with a Chinese vs. a US cultural background changed their decisions more when collaborating with robots that communicated implicitly vs. explicitly. We also found evidence that Chinese subjects were more negative in their attitude to robots and, as a result, relied less on the robot's advice. These findings suggest that cultural values affect responses to robots in collaborative situations and reinforce the importance of culturally sensitive design in HRI.

Even though adaptive (trainable) spam filters are a common example of systems that make (semi-)autonomous decisions on behalf of the user, trust in these filters has been underexplored. This paper reports a study of usage of spam filters in the daily workplace and user behaviour in training these filters (N=43). User observation, interview and survey techniques were applied to investigate attitudes towards two types of filters: a user-adaptive (trainable) and a rule-based filter. While many of our participants invested extensive effort in training their filters, training did not influence filter trust. Instead, the findings indicate that users' filter awareness and understanding seriously impacts attitudes and behaviour. Specific examples of difficulties related to awareness of filter activity and adaptivity are described showing concerns relevant to all adaptive and (semi-)autonomous systems that rely on explicit user feedback.

Despite robots' embodiment, the effect of physical contact or touch and its interaction with robots' autonomous behaviour has been a mostly overlooked aspect of human-robot interaction. This video-based, 2x2 between-subject survey experiment (N=119) found that touch and proactiveness interacted in their effects on perceived machine-likeness and dependability. Attitude towards robots in general also interacted with the effects of touch. Results show the value of further exploring the combination of physical aspects of human-robot interaction and proactiveness.

This study investigates the effects of group orientation and communication style on Chinese subjects' responsiveness to robots. A 2x2 experiment was conducted with group orientation (ingroup vs. outgroup) and communication style (implicit vs. explicit) as dimensions. The results confirm expectations that subjects with a Chinese cultural background are more responsive to robots that use implicit communication styles. We also found some evidence that subjects were more responsive when they thought of the robot as an ingroup member. These findings inform the design of robots for use in China and countries with similar cultural values and reinforce the importance of culturally sensitive design in HRI.

In this paper, we present ongoing research concerning the interaction between users and autonomous mobile agents in the environmental monitoring domain. The overarching project, DIADEM, deals with developing a system that detects potentially hazardous situations in populated industrial areas using input from both a distributed sensor network and humans through mobile devices. We propose a model of interaction with the gas detection system where concerned citizens communicate with a mobile agent to inform the gas monitoring system about unusual smells via their mobile phones. Next, we present a preliminary user requirements analysis based on 40 phone calls from members of the public to an environmental monitoring agency. Finally, we introduce measures to study the delicate long-term social relationship between users and the gas monitoring system.

The human robot interaction community is multidisciplinary by nature and has members from social science to engineering backgrounds. In this paper we aim to provide human robot developers with a straightforward toolkit to evaluate users' acceptance of assistive social robots they are designing or developing for elderly care environments. We will explain how we developed the measures for this analysis, provide do's and don'ts in designing the experiments, demonstrate the application of the measures we have developed for this purpose and the analysis and interpretation of the data. As such we hope to engage human robot interaction developers in evaluating the acceptability of their own robot to inform the development process and improve the final robot's design.

State of the art web search systems enable aggregation of information from many sources. Users are challenged to assess the reliability of information from different sources. We report on an empirical user study on the effect of displaying credibility ratings of multiple cultural heritage sources (e.g. museum websites, art blogs) on users' search performance and selection. The results of our online interactive study (N=122) show that when explicitly presenting these ratings, people become significantly more confident in their selection of information from aggregated results.

Abstract Embodied social agents are programmed to display human‐like social behaviour to increase intuitiveness of interacting with these agents. It is yet unclear what the differences in peoples' responses are to different types of agents' social behaviours. One example is touch. Despite robots' physical embodiment and increasing autonomy, the effect of communicative touch has been a mostly overlooked aspect of human‐robot interaction. This video‐based, 2 × 2 between‐subject survey experiment (N = 119) found that the combination of touch and proactivity influenced whether people saw the robot as machine‐like and dependable. Furthermore, participants' attitude toward robots in general was found to influence perceived closeness between a human and a robot. Results show that communicative touch could be considered a more appropriate behaviour for proactive agents rather than reactive agents. Also, people that are generally more positive towards robots find robots that interact by touch less machine‐like. These effects illustrate that careful consideration is necessary when incorporating social behaviours in agents' physical interaction design. Copyright © 2009 John Wiley & Sons, Ltd.

As robots (and other technologies) increasingly make decisions on behalf of people, it is important to understand how people from diverse cultures respond to this capability. Thus far, much design of autonomous systems takes a Western view valuing individual preferences and choice. We challenge the assumption that Western values are universally optimal for robots. In this study, we sought to clarify the effects of users' cultural background on human-robot collaboration by investigating their attitudes toward and the extent to which people accepted choices made by a robot or human assistant. A 2x2x2 experiment was conducted with nationality (US vs. Chinese), in group strength (weak vs. strong) and human vs. robot assistant as dimensions. US participants reported higher trust of and compliance with the assistants (human and robot) although when the assistant was characterized as a strong ingroup member, Chinese as compared with the US subjects were more comfortable. Chinese also reported a stronger sense of control with both assistants and were more likely to anthropomorphize the robot than were US subjects. This pattern of findings confirms that people from different national cultures may respond differently to robots, but also suggests that predictions from human-human interaction do not hold universally.

In this paper we explore the concept of enjoyment as a possible factor influencing acceptance of robotic technology by elderly people. We describe an experiment with a conversational robot and elderly users (n=30) that incorporates both a test session and a long term user observation. The experiment did confirm the hypothesis that perceived enjoyment has an effect on the intention to use a robotic system. Furthermore, findings show that the general assumption in technology acceptance models that intention to use predicts actual use is also applicable to this specific technology used by elderly people.

Summary form only given. The possibilities of using robots in eldercare have inspired a growing number of research projects. Not only can robotic technologies be used for rehabilitation, also they could facilitate the work of caregivers and provide social company for elders. In our project we are interested in the acceptance of robotic technology by elders and particularly in the influence of a robots social abilities on acceptance. Several studies on interaction with robots stress the importance of social intelligence and even more so in a health- and eldercare environment. It shows a more social intelligent robot will be more effective in its communication and it can therefore be expected to be easier and more pleasant to interact with and therefore would be indeed accepted easier.

When using a robot or a screen agent, elderly users might feel more enjoyment if they experience a stronger social presence. In two experiments with a robotic agent and a screen agent (both n=30) this relationship between these two concepts could be established. Besides, both studies showed that social presence correlates with the intention to use the system, although there were some differences between the agents. This implicates that factors that influence social presence are relevant when designing assistive agents for elderly people.

The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment ( N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study’s results.

This study examines the influence of perceived social abilities of a robot on user's attitude towards and acceptance of the robot. An interface robot with simulated conversational capabilities was used in a Wizard of Oz experiment with two conditions: a more socially communicative (the robot made use of a larger set of social abilities in interaction) and a less socially communicative interface. Participants (n=40) were observed in 5 minute interaction sessions and were asked to answer questions on perceived social abilities and technology acceptance. Results show that participants who were confronted with the more socially communicative version of the robot felt more comfortable and were more expressive in communicating with it. This suggests that the more socially communicative condition would be more likely to be accepted as a conversational partner. However, the findings did not show a significant correlation between perceived social abilities and technology acceptance