Child-robot Interaction Research Articles

Children\u2019s Imaginaries of Robots for Playing With

Children with severe motor impairment due to cerebral palsy have difficulties engaging in play, although they want to play games that typically developing children play. The barriers imposed by motor impairments against engaging in play can be addressed through the use of robots. We aim to identify how children, who have extensive experience of play, imagine what a robot is and what features would make a robot good to play with. Using a qualitative description design, 19 children from urban and rural settings participated in focus groups to draw and talk about the robots they would like to exist. The data were coded and analyzed using a summative approach to content analysis. The findings revealed that the children imagined that a good robot to play with is one that has an anthropomorphic appearance, is tough and strong, has controls, and that is able to move, grab, speak, and play popular children’s games. In particular, the girls imagined that robots should be able to express positive emotions towards children. Age, gender, culture, and the physical environment in which the children lived influenced what they expected to find in a robot for playing with and how they imagined child–robot interactions.

Estimating Children Engagement Interacting with Robots in Special Education Using Machine Learning

The task of child engagement estimation when interacting with a social robot during a special educational procedure is studied. A multimodal machine learning-based methodology for estimating the engagement of the children with learning difficulties, participating in appropriate designed educational scenarios, is proposed. For this purpose, visual and audio data are gathered during the child-robot interaction and processed towards deciding an engaged state of the child or not. Six single and three ensemble machine learning models are examined for their accuracy in providing confident decisions on in-house developed data. The conducted experiments revealed that, using multimodal data and the AdaBoost Decision Tree ensemble model, the children’s engagement can be estimated with 93.33% accuracy. Moreover, an important outcome of this study is the need for explicitly defining the different engagement meanings for each scenario. The results are very promising and put ahead of the research for closed-loop human centric special education activities using social robots.

Cultivating students’ computational thinking through student–robot interactions in robotics education

This research focuses on student–robot interaction in the learning environment of robotics education (RE) and attempts to explore how it cultivates students’ computational thinking (CT). Different from child–robot interactions as investigated in the social robot field, student–robot (S–R) interactions focus mainly on the process of interaction between learners and programmable robot kits in RE settings. At a four-week robotics summer camp in China, mixed-methods research was conducted. Forty primary school students and one dedicated robotics teacher participated in this research, while 32 students and the teacher completed all the lessons and data collection procedures of the summer camp. Results indicated that students’ CT skill increased during the summer camp and that the change in their CT skill was positively correlated to the time spent on S–R interaction. Additionally, how three kinds of S–R interaction—programming-computing, observational investigation, and participatory investigation—cultivated students’ CT were found. Moreover, the hierarchy of three S–R interactions and students’ role-shifting in the hierarchy were discussed. Previous studies rarely discussed S–R interaction; however, this kind of interaction should be explored because it provides more information about students’ natural learning process, which might be meaningful to RE practice.

Pivotal Response Treatment with and without robot-assistance for children with autism: a randomized controlled trial

Pivotal response treatment (PRT) is a promising intervention focused on improving social communication skills in children with autism spectrum disorder (ASD). Since robots potentially appeal to children with ASD and may contribute to their motivation for social interaction, this exploratory randomized controlled trial (RCT) was conducted comparing PRT (PRT and robot-assisted PRT) with treatment-as-usual (TAU). Seventy-three children (PRT: n = 25; PRT + robot: n = 25; TAU: n = 23) with ASD, aged 3–8 years were assessed at baseline, after 10 and 20 weeks of intervention, and at 3-month follow-up. There were no significant group differences on parent- and teacher-rated general social-communicative skills and blindly rated global functioning directly after treatment. However, at follow-up largest gains were observed in robot-assisted PRT compared to other groups. These results suggest that robot-assistance may contribute to intervention efficacy for children with ASD when using game scenarios for robot-child interaction during multiple sessions combined with motivational components of PRT. This trial is registered at https://www.trialregister.nl/trial/4487; NL4487/NTR4712 (2014-08-01).

Do Shy Preschoolers Interact Differently When Learning Language With a Social Robot? An Analysis of Interactional Behavior and Word Learning.

Temperamental traits can decisively influence how children enter into social interaction with their environment. Yet, in the field of child–robot interaction, little is known about how individual differences such as shyness impact on how children interact with social robots in educational settings. The present study systematically assessed the temperament of 28 preschool children aged 4–5 years in order to investigate the role of shyness within a dyadic child–robot interaction. Over the course of four consecutive sessions, we observed how shy compared to nonshy children interacted with a social robot during a word-learning educational setting and how shyness influenced children’s learning outcomes. Overall, results suggested that shy children not only interacted differently with a robot compared to nonshy children, but also changed their behavior over the course of the sessions. Critically, shy children interacted less expressively with the robot in general. With regard to children’s language learning outcomes, shy children scored lower on an initial posttest, but were able to close this gap on a later test, resulting in all children retrieving the learned words on a similar level. When intertest learning gain was considered, regression analyses even confirmed a positive predictive role of shyness on language learning gains. Findings are discussed with regard to the role of shyness in educational settings with social robots and the implications for future interaction design.

The wizard and I: How transparent teleoperation and self-description (do not) affect children\u2019s robot perceptions and child-robot relationship formation

It has been well documented that children perceive robots as social, mental, and moral others. Studies on child-robot interaction may encourage this perception of robots, first, by using a Wizard of Oz (i.e., teleoperation) set-up and, second, by having robots engage in self-description. However, much remains unknown about the effects of transparent teleoperation and self-description on children’s perception of, and relationship formation with a robot. To address this research gap initially, we conducted an experimental study with a 2 × 2 (teleoperation: overt/covert; self-description: yes/no) between-subject design in which 168 children aged 7–10 interacted with a Nao robot once. Transparency about the teleoperation procedure decreased children’s perceptions of the robot’s autonomy and anthropomorphism. Self-description reduced the degree to which children perceived the robot as being similar to themselves. Transparent teleoperation and self-description affected neither children’s perceptions of the robot’s animacy and social presence nor their closeness to and trust in the robot.

"It Is Not the Robot Who Learns, It Is Me." Treating Severe Dysgraphia Using Child-Robot Interaction.

Writing disorders are frequent and impairing. However, social robots may help to improve children's motivation and to propose enjoyable and tailored activities. Here, we have used the Co-writer scenario in which a child is asked to teach a robot how to write via demonstration on a tablet, combined with a series of games we developed to train specifically pressure, tilt, speed, and letter liaison controls. This setup was proposed to a 10-year-old boy with a complex neurodevelopmental disorder combining phonological disorder, attention deficit/hyperactivity disorder, dyslexia, and developmental coordination disorder with severe dysgraphia. Writing impairments were severe and limited his participation in classroom activities despite 2 years of specific support in school and professional speech and motor remediation. We implemented the setup during his occupational therapy for 20 consecutive weekly sessions. We found that his motivation was restored; avoidance behaviors disappeared both during sessions and at school; handwriting quality and posture improved dramatically. In conclusion, treating dysgraphia using child–robot interaction is feasible and improves writing. Larger clinical studies are required to confirm that children with dysgraphia could benefit from this setup.

A Meta-analysis on Children\u2019s Trust in Social Robots

Although research on children’s trust in social robots is increasingly growing in popularity, a systematic understanding of the factors which influence children’s trust in robots is lacking. In addition, meta-analyses in child–robot-interaction (cHRI) have yet to be popularly adopted as a method for synthesising results. We therefore conducted a meta-analysis aimed at identifying factors influencing children’s trust in robots. We constructed four meta-analytic models based on 20 identified studies, drawn from an initial pool of 414 papers, as a means of investigating the effect of robot embodiment and behaviour on both social and competency trust. Children’s pro-social attitudes towards social robots were also explored. There was tentative evidence to suggest that more human-like attributes lead to less competency trust in robots. In addition, we found a trend towards the type of measure that was used (subjective or objective) influencing the direction of effects for social trust. The meta-analysis also revealed a tendency towards under-powered designs, as well as variation in the methods and measures used to define trust. Nonetheless, we demonstrate that it is still possible to perform rigorous analyses despite these challenges. We also provide concrete methodological recommendations for future research, such as simplifying experimental designs, conducting a priori power analyses and clearer statistical reporting.

Exploring the Role of Trust and Expectations in CRI Using In-the-Wild Studies

Studying interactions of children with humanoid robots in familiar spaces in natural contexts has become a key issue for social robotics. To fill this need, we conducted several Child–Robot Interaction (CRI) events with the Pepper robot in Polish and Japanese kindergartens. In this paper, we explore the role of trust and expectations towards the robot in determining the success of CRI. We present several observations from the video recordings of our CRI events and the transcripts of free-format question-answering sessions with the robot using the Wizard-of-Oz (WOZ) methodology. From these observations, we identify children’s behaviors that indicate trust (or lack thereof) towards the robot, e.g., challenging behavior of a robot or physical interactions with it. We also gather insights into children’s expectations, e.g., verifying expectations as a causal process and an agency or expectations concerning the robot’s relationships, preferences and physical and behavioral capabilities. Based on our experiences, we suggest some guidelines for designing more effective CRI scenarios. Finally, we argue for the effectiveness of in-the-wild methodologies for planning and executing qualitative CRI studies.

An Architecture for Safe Child–Robot Interactions in Autism Interventions

Autism Spectrum Disorder is a developmental disorder that affects children from a very young age and is characterized by persistent deficits in social, communicational, and behavioral abilities. Since there is no cure for autism, domain experts focus on aiding these children through specific intervention plans that are aimed towards the development of the deficient areas. Using socially assistive robots that interact in a social manner with children in autism interventions, efforts are being made towards alleviating the autistic behavior of children and enhancing their social behavior. However, implementing robots in autism interventions could lead to harmful situations concerning safety. In this paper, an architecture for safe child–robot interactions in autism interventions is proposed. First, a taxonomy of child–robot interactions in autism interventions is presented, explaining its complete framework. Next, the interaction is modelled according to this taxonomy where an interaction case is employed in order for the structure of the interaction to be defined. Based on that, the safety architecture is proposed that will be integrated into the robot’s controller. Focus is placed on detecting possible distracting elements that could influence the performance of the child, affecting their psychological or physical safety. Lastly, the interaction between child and robot is created in a simulated environment through dialogue inputs and outputs, and the code of the architecture is tested, where a virtual robot performs the appropriate actions.

Comparing the Effects of a Different Social Partner (Social Robot vs. Human) on Children's Social Referencing in Interaction

Social robots have emerged as a new digital technology that is increasingly being implemented in the educational landscape. While social robots could be deployed to assist young children with their learning in a variety of different ways, the typical approach in educational practices is to supplement the learning process rather than to replace the human caregiver, e.g., the teacher, parent, educator or therapist. When functioning in the role of an educational assistant, social robots will likely constitute a part of a triadic interaction with the child and the human caregiver. Surprisingly, there is little research that systematically investigates the role of the caregiver by examining the ways in which children involve or check in with them during their interaction with another partner—a phenomenon that is known as social referencing. In the present study, we investigated social referencing in the context of a dyadic child–robot interaction. Over the course of four sessions within our longitudinal language-learning study, we observed how 20 pre-school children aged 4–5 years checked in with their accompanying caregivers who were not actively involved in the language-learning procedure. The children participating in the study were randomly assigned to either an interaction with a social robot or a human partner. Our results revealed that all children across both conditions utilized social referencing behaviors to address their caregiver. However, we found that the children who interacted with the social robot did so significantly more frequently in each of the four sessions than those who interacted with the human partner. Further analyses showed that no significant change in their behavior over the course of the sessions could be observed. Findings are discussed with regard to the caregiver's role during children's interactions with social robots and the implications for future interaction design.

Are friends electric? The benefits and risks of human-robot relationships.

SummarySocial robots that can interact and communicate with people are growing in popularity for use at home and in customer-service, education, and healthcare settings. Although growing evidence suggests that co-operative and emotionally aligned social robots could benefit users across the lifespan, controversy continues about the ethical implications of these devices and their potential harms. In this perspective, we explore this balance between benefit and risk through the lens of human-robot relationships. We review the definitions and purposes of social robots, explore their philosophical and psychological status, and relate research on human-human and human-animal relationships to the emerging literature on human-robot relationships. Advocating a relational rather than essentialist view, we consider the balance of benefits and harms that can arise from different types of relationship with social robots and conclude by considering the role of researchers in understanding the ethical and societal impacts of social robotics.

The Effects of Long-Term Child–Robot Interaction on the Attention and the Engagement of Children with Autism

Using a social robot has been proven to have multiple benefits for the training of children with Autism Spectrum Disorder (ASD). However, there is no clarity on the impact of the interaction quality between a child with ASD and a robot on the effectiveness of the therapy. Previous research showed that the use of a robot in Pivotal Response Treatment (PRT) could be an effective treatment component in diminishing ASD-related symptoms. Further analyzing the data from a randomized controlled trial of PRT treatment, we looked at the long-term effects of child–robot game interactions to see whether the interaction quality changes over time. The attention and the engagement of six children were measured through the observation of non-verbal behavior at three different stages in the treatment that took 20 sessions per child. The gaze and arm/hand behavior of the participants towards the robot, the game, and other present humans were observed. The analysis showed no significant decrease in the attention and the engagement of the children towards the robot and the game. However, the attention and engagement toward the parents of the children increased. We conclude that the main result of sustained attention and engagement with the robot is due to the personalization of the games to meet the specific needs of this user group. These specific needs are met through inclusion of variability to the level of development and personal choice of each participating child. We see the additional finding of increased attention towards the parents as especially positive since the children are expected to improve in human–human interaction as a result of this treatment.

Child-Robot Interaction in a Musical Dance Game: An Exploratory Comparison Study between Typically Developing Children and Children with Autism

ABSTRACT Using robots in therapy for children on the autism spectrum is a promising avenue for child-robot interaction, and one that has garnered significant interest from the research community. After preliminary interviews with stakeholders and evaluating music selections, twelve typically developing (TD) children and three children with Autism Spectrum Disorder (ASD) participated in an experiment where they played the dance freeze game to four songs in partnership with either a NAO robot or a human partner. Overall, there were significant differences between TD children and children with ASD (e.g., mimicry, dance quality, & game play). There were mixed results for TD children, but they tended to show greater engagement with the researcher. However, objective results for children with ASD showed greater attention and engagement while dancing with the robot. There was little difference in game performance between partners or songs for either group. However, upbeat music did encourage greater movement than calm music. Using a robot in a musical dance game for children with ASD appears to show the advantages and potential just as in previous research efforts. Implications and future research are discussed with the results.

Using social robot PLEO to enhance the well-being of hospitalised children.

Hospitalisation is stressful for children. Play material is often offered for distraction and comfort. We explored how contact with social robot PLEO could positively affect a child's well-being. To this end, we performed a multiple case study on the paediatric ward of two hospitals. Child life specialists offered PLEO as a therapeutic activity to children in a personalised way for a well-being related purpose in three to five play like activity sessions during hospital visits/stay. Robot-child interaction was observed; care professionals, children and parents were interviewed. Applying direct content analysis revealed six categories of interest: interaction with PLEO, role of the adults, preferences for PLEO, PLEO as buddy, attainment of predetermined goal(s) and deployment of PLEO. Four girls and five boys, aged 4-13, had PLEO offered as a relief from stress or boredom or for physical stimulation. All but one started interacting with PLEO and showed behaviours like hugging, caring or technical exploration, promoting relaxation, activation and/or making contact. Interaction with PLEO contributed to achieving the well-being related purpose for six of them. PLEO was perceived as attractive to elicit play. Although data are limited, promising results emerge that the well-being of hospitalised children might be fostered by a personalised PLEO offer.

Designing an authoring system of robots and IoT-based toys for EFL teaching and learning

This study aims to design an authoring system of robots and IoT-based toys for creating a scenario-based interactive learning environment for young English as a Foreign Language learners. This study adopts a design-based research approach to investigate the pedagogical needs, the critical features and usability of such a authoring system. Twelve adult participants, including EFL teachers, parents, non-technical, and technical professionals, voluntarily participated in this study to co-develop and evaluated the authoring system through iterations of needs collection, system design, system testing and refinements. An authoring system, consisting of IoT-based toys, a robot and a script editor, was finally developed taking four cycles of iterative improvements in 18 weeks. Eleven pedagogical needs concerning children’s language and cognitive development, better children-robot interaction and practical issues were identified. The modifications made across different versions of the authoring system were presented in detail. The results also show that the issues of effectiveness, robustness and efficiency were the most frequently identified, and that the system had achieved good usability after four rounds of design and refinements. Design principles and guidelines also merged as a result providing insights into the effective design of Robot Assisted Language Learning in particular and Technology-Enhanced Language Learning in general.

Reading with robot and human companions in library literacy activities: A comparison study

AbstractMotivated by mixed evidence on the effectiveness of reading companions on children’s reading performance, as well as the clear and present need for libraries to conduct literacy education, this study sought to investigate the feasibility of using social robots in library literacy activities and to extract the essential functions of effective reading companions by comparing human and robot co‐readers. A humanoid robot, Robot Julia, was developed as a social robot to provide oral conversation and offer children tactful and stimulating support for their reading. An experimental study was conducted to examine child patrons’ perceptions and performance in reading activities with the robot companion as compared to human companions. A total of 36 elementary school children participated in the study. The results positively supported that the participants perceived the robot companion as more favourable and desirable to read with than a human co‐reader. The children favoured robotic verbalisation over human verbalisation. According to the results of the comparison, cognitively it was found that human and robot companions facilitated children’s reading comprehension in different ways and that the children performed similarly well with both kinds of reading companions. Affectively, the robot co‐reader induced more social interaction during the reading sessions. Despite all the positive aspects, it is also necessary to consider the limitations including the novelty effect of the approach. This study contributes empirical evidence in the pragmatic field of library science and expands upon social robot research by exploring one‐on‐one child–robot interactions in reading, as opposed to the group contexts in previous studies.

Special Issue on Behavior Adaptation, Interaction, and Artificial Perception for Assistive Robotics

This special issue aims at examining and promoting recent developments in the Assistive/Social Robotics field and future directions including the related challenges and how these can be overcome with particular focus on computational intelligence methodologies. With robots being integrated more and more in our environments and daily activities, the effectiveness of robotics applications has to rely on robots' ability to perceive, reason, and adapt on-the-fly to the users' behavior and needs. In particular, the development of personal robots, as assistive technological tools, challenges researchers to develop socially intelligent and adaptive robots that can collaborate with people in real environment. This special issue is comprised of 14 papers, fostering discussions and investigations in robot behavior adaptation and learning, in socially assistive robotics, and in developing reliable systems for natural interactions between humans and robots. Several aspects and perspectives are taken into consideration by the authors covering various topics such as adaptability during real-world human-robot interactions ; new reinforcement learning and learning by teaching techniques especially used in the context of child-robot interaction ; social robot responses, perception and adaptation by means of a semantic representation; robots acceptability and security. Furthermore, several assistive applications for the elderly, children, and people are presented. B Mariacarla Staffa

The study of attention estimation for child-robot interaction scenarios

One of the biggest challenges in human-agent interaction (HAI) is the development of an agent such as a robot that can understand its partner (a human) and interact naturally. To realize this, a system (agent) should be able to observe a human well and estimate his/her mental state. Towards this goal, in this paper, we present a method of estimating a child's attention, one of the more important human mental states, in a free-play scenario of child-robot interaction (CRI). To realize attention estimation in such CRI scenario, first, we developed a system that could sense a child's verbal and non-verbal multimodal signals such as gaze, facial expression, proximity, and so on. Then, the observed information was used to train a model that is based on a Support Vector Machine (SVM) to estimate a human's attention level. We investigated the accuracy of the proposed method by comparing with a human judge's estimation, and obtained some promising results which we discuss here.

The Mind in the Machine: Mind Perception Modulates Gaze Aversion During Child–Robot Interaction

This study examined whether interacting with a humanoid robot influences children’s gaze aversion, an effortless strategy that people commonly use to facilitate thinking when asked challenging questions. Following the intentional stance model, we hypothesized that interacting with agents perceived as having a mind would modulate the social relevance assigned by the children to their interlocutor. Accordingly, we expected to observe an increase in children’s gaze aversion rates when questioned by an interaction partner believed to have a mind, compared to interaction conditions in which the questioner was believed to be a machine. To test this hypothesis, we involved 94 children in two experiments. In Experiment 1, the children interacted either with a humanoid robot (Human–Robot; n = 22) or with a human (Human–Human; n = 22) questioner. In Experiment 2, all the children interacted with a humanoid robot: one group was told the robot was controlled by a human (Avatar; n = 25), while the other group was told the robot was controlled by a computer algorithm (Machine; n = 25). Our results show that: (1) adopting an intentional stance (Human–Human; Avatar) increases gaze aversion rates; (2) gaze aversion increases and (3) response accuracy decreases as a function of question difficulty; (4) accuracy does not differ between interaction conditions. Based on these findings, we propose that gaze aversion rates might be considered an objective behavioural indicator of mind perception. Implications for robot-mediated education are also discussed.