Italy

Socially Assistive Robots (SARs) show promise to help children during therapeutic and clinical interventions.
However, using SARs for the evaluation of mental wellbeing of children has not yet been explored. Thus, this paper presents an empirical study with 28 children of 8-13 years old interacting with a Nao robot in a 45-minute session where the robot administered (robotised) the Short Mood and Feelings Questionnaire (SMFQ) and the Revised Child Anxiety and Depression Scale (RCADS). Prior to the experimental session, we also evaluated children’s wellbeing using established standardised approaches via online RCADS questionnaires filled by the children (self-report) and their parents (parent-report). We clustered the
participants into three groups (lower, medium, and higher tertile) based on their SMFQ scores. Further, we analysed the questionnaire responses
across the three clusters and across the different modes of administration (self-report, parent-report, and robotised).
Our results show that the robotised evaluation seems to be the most suitable mode in identifying wellbeing related anomalies in children across the three clusters of participants as compared with the self-report and the
parent-report modes. Further, children with decreasing levels of wellbeing (lower, medium and higher tertiles)
exhibit different response patterns: higher tertile are more negative in their responses with the robot while the lower tertile are more positive in their responses with the robot. Findings from this work show that SARs can be used to proactively evaluate mental wellbeing 
related concerns in children.

IEEE RO-MAN 2022

Can Robots Help in the Evaluation of Mental Wellbeing in Children? An Empirical Study

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

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31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

technical paper

Previous research has shown that robot mistakes or malfunctions have a significant negative impact on people’s trust. One way to mitigate the negative impact of trust violation is through trust repair. Although trust repair has been studied extensively, it is still not known which strategy is effective in repairing trust in a time-sensitive driving scenario. Additionally, prior research on trust repair has not dealt with the effects of expressing emotion in attempting trust repair. In this paper, we presented the development of a variety of trust repair methods for a time-sensitive scenario using a simulated driving environment as a testbed for validation. These trust repair methods included baseline apology, emotional apology, and explanation. We conducted an experiment to compare the impact of these trust repair methods on human-robot trust. Experimental results indicated that the emotional apology positively affected more participants than the no-repair, baseline apology, and explanation. Furthermore, this study identified emotional apology as the most effective method for the time-sensitive driving scenario.

Evaluating the Impact of Emotional Apology on Human-Robot Trust

Handwriting learning is a long and complex process that takes about ten years to be fully mastered. Nearly one-third of all children aged 4-12 experiences handwriting difficulties and, sadly, most of them are left to fight them on their own, due to the scarcity of tools for the detection and remediation of such difficulties. Building on state-of-the-art digital solutions for automated handwriting assessment and the training of specific handwriting-related skills, in this article we discuss requirements, rationale, and architecture of a system for handwriting training, which relies on a social robot as a mediator agent, offering personalized training and suggestions. The system is envisioned to operate autonomously and to support long-term interactions via personalization. Preliminary validation of the system in an experiment with 31 children showed its potential not only for autonomously guiding handwriting training sessions, but also for its inclusion in the teachers' practice.

A game-based approach for evaluating and customizing handwriting training using an autonomous social robot

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

Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots for Groups with Teenagers

Robots are increasingly involved in tasks that require them to navigate social spaces shared with humans.
Following social norms is considered a key requirement for such robots to ensure their acceptance and long-term use. This paper focuses on delivery robots as these often encounter problems in their operational areas–in this case a busy university campus–
when they find their way blocked by people and they cannot move on towards their goal destination. We explored automated cue triggering to resolve this situation autonomously without the help of remote operators. Eighty-three pedestrians participated
in a real-world study using a delivery robot. Four different cues were tested for their perceived politeness and ambiguity. The four different cues differed in the presence or absence of an instruction and the presence or absence of a justification for the request to let the robot pass as well as source orientation within the justification, which was either internally (self-)
directed or externally (user-) directed. The results reveal a complex picture. Overall, a positive effect of verbal instructions in comparison to staying mute on social attributions to the
robot was found. Contrary to our expectations, there was no significant difference in politeness between the different requests. Participants’ willingness to let the robot pass was
positively correlated with perceived politeness, and negatively correlated with ambiguity of the requests.

Polite and Unambiguous Requests Facilitate Willingness to Help an Autonomous Delivery Robot and Favourable Social Attributions

A pre-registered conceptual video-based replication of a laboratory experiment was conducted to test whether the impact of a robot’s non-functional touch to a human can be studied from observation (online). Therefore, n=92 subjects watched either a video recording of the same human– robot interaction with or without touch. The interpretation, evaluation, and emotional as well as behavioral reactions were collected by means of an online-survey. The results show that the observation of touch affects observers’ emotional state: Contrary to what was hypothesized, observers felt significantly better when no touch was visible. In addition, observers evaluated the robot’s touch as inappropriate. The findings are compared to results from a laboratory experiment and the discrepancies are discussed in the light of the usefulness of video- based HRI investigations (for the study of touch).


Seeing is not Feeling the Touch from a Robot

In this study, we describe an improved version of our proposed model to generate gaze movements (eye and head movements) of a dialogue robot in multi-party dialogue situations, and investigated how the impressions change for models created by data of speakers with different personalities. For that purpose, we used a multimodal three-party dialogue data, and first analyzed the distributions of (1) the gaze target (towards dialogue partners or gaze aversion), (2) the gaze duration, and (3) the eyeball direction during gaze aversion. We then generated gaze behaviors in an android robot (Nikola) with the data of two people who were found to have distinctive personalities, and conducted subjective evaluation experiments. Results showed that a significant difference was found in the perceived personalities between the motions generated by the two models.

Expression of Personality by Gaze Movements of an Android Robot in Multi-Party Dialogues

Recent studies with children have pointed out the importance of spatial thinking as an essential factor in determining later success in STEM-related fields. The current study explores the potential of using embodied activities with robots to aid the development of children’s spatial perspective-taking abilities. This research focuses on evaluating children’s spatial perspective-taking abilities and assessing the potential of the designed activity to practice perspective-taking. The activity design is inspired by the dynamic and mental processes involved in remote-controlled cars and racing games, it is developed with a Cozmo robot, and it includes guiding the robot within the maze by considering the robot’s point of view. We evaluated the activity through a user study with 22 elementary school children between the ages of 8 and 9. The findings showed that children’s performance at different angular disparities was aligned with the previous research in developmental psychology. Additionally, most children made fewer mistakes in guiding the robot as they played more. Finally, while we did not observe any performance improvement in the group of children who had access to the robot’s point of view during the game, we learned new insights about how children perceived seeing the maze through the robot’s eyes.

Motivating Children to Practice Perspective-Taking Through Playing Games with Cozmo

In today's world, it is not uncommon to see robots adopted in various domains and environments. Robots take over several roles and tasks from manufacturing facilities to households and offices. It is crucial to measure people's judgment of robots' social attributes since the findings can shape the future design for social robots. Using only a simple and mono-functional robotic vacuum cleaner, this paper investigates the impact of expressive motions on how people perceive the social attributes of the robot. The Laban Effort Features, a framework for movement analysis that emerged from dance, was modified to design expressive motions for a simple cleaning task. Participants were asked to rate the social attributes of the robot under several treatment conditions using a video-based online survey. The results indicated that velocity influenced people's ratings of the robot's warmth and competence, while path planning behavior influenced people's ratings of the robot's competence and discomfort. Limitations of this study include the kinematic constraints of the robot, potential issues with survey design, and technical constraints related to the open interface provided by the robot’s developer. The findings should be considered when incorporating expressive motions into domestic service robots operating in social settings.

Evaluation of Expressive Motions based on the Framework of Laban Effort Features for Social Attributes of Robots

In this paper, we study how a co-located robot affects human motivation and emotion. In particular, we ex- amine the role of the co-located robot’s anthropomorphism, as well as the effects of the human’s personality and gender. To study this, we conducted an online experiment, where 182 participants completed a repetitive task, either easy or hard, in one of the four conditions: in the presence of a non-anthropomorphic robot, an anthropomorphic robot, another human, or alone. For each condition, we analyzed the number of repetitions and the total time users spent, which we treated as the proxy of their motivation, as well as their self-reported emotional states. The study results suggest that the presence of a non-anthropomorphic robot has the potential to lead to a higher level of motivation and a more desirable affective state for users than the presence of an anthropomorphic robot or another human, especially for introverts and female users during difficult tasks.

Norm Learning with Reward Models from Instructive and Evaluative Feedback

People are increasingly interacting with artificial agents in social settings, and as these agents become more sophisticated, people will have to teach them social norms. Two prominent teaching methods include instructing the learner how to act, and giving evaluative feedback on the learner’s actions. Our empirical findings indicate that people naturally adopt both methods when teaching norms to a simulated robot, and they use the methods selectively as a function of the robot’s perceived expertise and learning progress. In our algorithmic work, we conceptualize a set of context-specific norms as a reward function and integrate learning from the two teaching methods under a single likelihood-based algorithm, which estimates a reward function that induces policies maximally likely to satisfy the teacher’s intended norms. We compare robot learning under various teacher models and demonstrate that a robot responsive to both teaching methods can learn to reach its goal and minimize norm violations in a navigation task for a grid world. We improve the robot’s learning speed and performance by enabling teachers to give feedback at an abstract level (which rooms are acceptable to navigate) rather than at a low level (how to navigate any particular room).

Can Robots Help in the Evaluation of Mental Wellbeing in Children? An Empirical Study

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Evaluating the Impact of Emotional Apology on Human-Robot Trust

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