Cristina Zaga

The Effect of a Robot's Social Character on Children Task Engagement: Peer Versus Tutor

An increasing number of applications for social robots focuses on learning and playing with children. One of the unanswered questions is what kind of social character a robot should have in order to positively engage children in a task. In this paper, we present a study on the effect of two different social characters of a robot (peer vs. tutor) on children’s task engagement. We derived peer and tutor robot behaviors from the literature and we evaluated the two robot characters in a WoZ study where 10 pairs of children aged 6 to 9 played Tangram puzzles with a Nao robot. Our results show that in the peer character condition, children paid attention to the robot and the task for a longer period of time and solved the puzzles quicker and better than in the tutor character condition.

A Simple Nod of the Head: The Effect of Minimal Robot Movements on Children's Perception of a Low-Anthropomorphic Robot

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 childrens perception of animacy and likeability of the robot. Deictic-gaze and social-gaze movements significantly increased childrens 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.

Experts get me started, peers keep me going: comparing crowd- versus expert-designed motivational text messages for exercise behavior change

We present a comparative analysis of motivational messages designed with a theory-driven approach. A previous study [4] involved crowdsourcing to design and evaluate motivational text messages for physical activity, and showed that these peer-designed text messages aligned to behavior change strategies from theory. However, the messages were predominantly rated as motivating in the later stages of behavior change, not in the earlier stages, including those strategies intended for the earlier stages. We speculated that the peers that designed the messages aligned to the strategies did not have sufficient expertise to motivate people in earlier stages. Therefore, we replicated the study with experts. We found that for two of the strategies expert-designed messages were found more motivating in the earliest stage, while for several of the strategies peer-designed messages were rated more motivating for later stages. We conclude that when using these strategies in behavior change technology, expert-designed messages could be more motivating in the earliest stage, while peer-designed messages could be more motivating in the later stages.

Help-Giving Robot Behaviors in Child-Robot Games: Exploring Semantic Free Utterances

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.

A word of advice: how to tailor motivational text messages based on behavior change theory to personality and gender

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.

2nd Workshop on Evaluating Child Robot Interaction

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.

Growing-Up Hand in Hand with Robots: Designing and Evaluating Child-Robot Interaction from a Developmental Perspective

Robots are becoming part of childrens care, entertainment, education, social assistance and therapy. A steadily growing body of Human-Robot Interaction (HRI) research shows that child-robot interaction (CRI) holds promises to support childrens development in novel ways. However, research has shown that technologies that do not take into account childrens needs, abilities, interests, and developmental characteristics may have a limited or even negative impact on their physical, cognitive, social, emotional, and moral development. As a result, robotic technology that aims to support children via means of social interaction has to take the developmental perspective into consideration. With this workshop (the third of a series of workshops focusing CRI research), we aim to bring together researchers to discuss how a developmental perspective play a role for smart and natural interaction between robots and children. We invite participants to share their experiences on the challenges of taking the developmental perspective in CRI, such as long-term sustained interactions in the wild, involving children and other stakeholders in the design process and more. Looking across disciplinary boundaries, we hope to stimulate thought-provoking discussions on epistemology, methods, approaches, techniques, interaction scenarios and design principles focused on supporting childrens development through interaction with robotic technology. Our goal does not only focus on the conception and formulation of the outcomes in the context of the workshop venue, but also on their establishment and availability for the HRI community in different forms

Snoozle -- A Robotic Pillow That Helps You Go to Sleep: HRI 2017 Student Design Competition

Not getting enough sleep is detrimental to our health and productivity, yet we have difficulty to maintain consistent bedtimes. Technological solutions to this problem mostly focus on detecting sleep patterns and providing feedback on them. We felt there was an opportunity for a perspective that concentrates on ones subjective experience. We propose Snoozle, an actuated pillow that supports consistent bedtimes by inviting users to bed, and improves the sleeping experience by enhancing the feeling of co-presence. In this proposal, we present how the concept of Snoozle developed from structured brainstorms, storyboards and sketches. We discuss the actuated pillow behavior and the envisioned interaction, and we detail our next steps.

Multimodal Detection of Engagement in Groups of Children Using Rank Learning

In collaborative play, children exhibit different levels of engagement. Some children are engaged with other children while some play alone. In this study, we investigated multimodal detection of individual levels of engagement using a ranking method and non-verbal features: turn-taking and body movement. Firstly, we automatically extracted turn-taking and body movement features in naturalistic and challenging settings. Secondly, we used an ordinal annotation scheme and employed a ranking method considering the great heterogeneity and temporal dynamics of engagement that exist in interactions. We showed that levels of engagement can be characterised by relative levels between children. In particular, a ranking method, Ranking SVM, outperformed a conventional method, SVM classification. While either turn-taking or body movement features alone did not achieve promising results, combining the two features yielded significant error reduction, showing their complementary power.