Beyond Robots - embodying the fantasy and piling up the imagination.

Collaborative storytelling has long been a goal of social robotics, however, much of this research is limited in interactivity or assumes that story content is curated. In this paper, we present a working fully-automatic collaborative storytelling robot, which can collaborate with a person to create a unique, improvised story by using a large-scale neural language model to dynamically generate continuations to a story. Because effective storytelling requires engaging the emotions of participants, we explore several modalities of procedurally-generated expressivity: 1. an expressive text-to-speech voice with several delivery styles, 2. physical and verbal reactions performed by the robot, and 3. an external display used to show instructions and graphics during storytelling.

To understand the issues associated with improvised collaborative storytelling with a social robot, we conduct an online survey and elicitation study with a group of online observers of collaborative storytelling gameplay, comparing several expressivity strategies in terms of storytelling-related characteristics, expressivity characteristics, and personality traits as measured by RoSAS. This evaluation showed that expressivity strategies using both emotive voice and performed reactions were perceived to be more competent storytellers and more strongly associated with positive personality traits.

Robot-mediated Group Instruction for Children with ASD: A Pilot Study

Regular exercise provides many mental and physical health benefits. However, when exercises are done incorrectly, it can lead to injuries. Because the COVID-19 pandemic made it challenging to exercise in communal spaces, the growth of virtual fitness programs was accelerated, putting people at risk of sustaining exercise-related injuries as they received little to no feedback on their exercising techniques. Co-located robots could be one potential enhancement to virtual training programs as they can cause higher learning gains, more compliance, and more enjoyment than non-co-located robots. In this study, we compare the effects of a physically present robot by having a person exercise either with a robot (robot condition) or a video of a robot displayed on a tablet (tablet condition). Participants (N=25) had an exercise system in their homes for two weeks. Participants who exercised with the co-located robot made fewer mistakes than those who exercised with the video-displayed robot. Furthermore, participants in the robot condition reported a higher fitness increase and more motivation to exercise than participants in the tablet condition.

Privacy Expectations for Human-Autonomous Vehicle Interactions

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

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

The supervision component is the binder of a robotic architecture. Without it, there is no task, no interaction happening, it conducts the other components of the
architecture towards the achievement of a goal, which means, in the context of a collaboration with a human, to bring changes in the physical environment and to update the human partner mental state. However, not so much work focus
on this component in charge of the robot decision-making and control, whereas this is the robot puppeteer. Most often, either tasks are simply scripted, or the supervisor
is built for a specific task. Thus, we propose JAHRVIS, a Joint Action-based Human-aware supeRVISor. It aims at being
task-independent while implementing a set of key joint action and collaboration mechanisms. With this contribution, we intend to move the deployment of autonomous collaborative robots forward, accompanying this paper with our open-source code.

Nothing About Us Without Us: a participatory design for an Inclusive Signing Tiago Robot

Study reproducibility and generalizability of results to broadly inclusive populations is crucial in any research. Previous meta-analyses in HRI have focused on the consistency of reported information from papers in various categories. However, members of the HRI community have noted that much of the information needed for reproducible and generalizable studies is not found in published papers. We address this issue by surveying the reported study metadata over the main proceedings of the 2021 IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) and the past three years (2019 through 2021) of the main proceedings of the International Conference on Human-Robot Interaction (HRI) and alt.HRI. Based on the analysis results, we propose a set of recommendations for the HRI community that follow the longer-standing reporting guidelines from human-computer interaction (HCI), psychology, and other fields most related to HRI. Finally, we examine two key areas for user study reproducibility: recruitment details and participant compensation. We find a lack of reporting of both of these study metadata categories: of the 414 studies across both conferences and all years, 258 studies failed to report recruitment method and 255 studies failed to report compensation. This work provides guidance about specific types of needed reporting improvements for the field of HRI.

A Review and Recommendations on Reporting Recruitment and Compensation Information in HRI Research Papers

Theory of Mind is often characterized as the ability to recognize desires, beliefs, and intentions of others. In this position paper, I look at the literature on modeling Theory of Mind in machines and find that, to date, intention is not usually a focus. I define what I mean by intention---choice with commitment---following prior work. Intention has a long history of research in some communities, and I offer one theoretical framework for modeling intention as a starting point. I take inspiration from how children learn intention through joint attention with others and how that leads to Theory of Mind. I argue that though models of machine Theory of Mind need not follow the same learning progression as children, intention is an aspect of Theory of Mind that should be more explicit.

AI bias in Human-Robot Interaction: An evaluation of the Risk in Gender Biased Robots

This study explored the culture and gender differences among older adults interacting with polite non-humanoid robots. We have used Lakoff theory for the polite conversational maxims. A within-subjects experiment was designed with polite and non-polite, correct and erroneous behaving robots. The polite robot employs three sub-rules (“don’t impose,” “give options,” and “be friendly”). A user study was conducted with older adults from two cultural backgrounds: Israeli Jewish and Arab participants. The study revealed that the participants could not differentiate between the polite behaviors when the robot was correct. They were more annoyed with a polite robot making an error as compared to a non-polite erroneous robot. Whereas gender had no impact on participants’ evaluations, there were significant cultural differences between the groups: Although the Arab participants were primarily more skeptical, they enjoyed, were satisfied, and trusted the robot more than the Jewish participants. In addition, they preferred the direct non-polite behavior, whereas the Jewish participants liked the polite behavior more.

A Non-Humanoid Robotic Object for Providing a Sense Of Security

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.

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

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

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.

Underline digital video library

Beyond Robots - embodying the fantasy and piling up the imagination.

Robot-mediated Group Instruction for Children with ASD: A Pilot Study

Privacy Expectations for Human-Autonomous Vehicle Interactions

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

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

Nothing About Us Without Us: a participatory design for an Inclusive Signing Tiago Robot

A Review and Recommendations on Reporting Recruitment and Compensation Information in HRI Research Papers

AI bias in Human-Robot Interaction: An evaluation of the Risk in Gender Biased Robots

A Non-Humanoid Robotic Object for Providing a Sense Of Security

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

Norm Learning with Reward Models from Instructive and Evaluative Feedback

Evaluating the Impact of Emotional Apology on Human-Robot Trust

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