Sofia Petisca

Social Robots for Older Adults: Framework of Activities for Aging in Place with Robots

According to the United Nations World Population Prospects, the world’s population is aging. Older adults constitute a fragile part of society, as aging is always accompanied by major psychological and physical challenges. A way to cope with those challenges is to strive for a good Quality of Life (QoL) and contribute to successful aging. Social robots can play an important role in the promotion of QoL by integrating activities with independent-living older adults. Using a qualitative design through a focus group method, this paper aims to present the activities in which independent-living older adults, i.e., older adults that do not depend upon anyone to carry out their activities, require a robot. By understanding the activities where robots can positively influence and contribute to older adults’ QoL, we set specific goals for the future research in the field of Human-Robot Interaction (HRI).

The case of classroom robots: teachers’ deliberations on the ethical tensions

Robots are increasingly being studied for use in education. It is expected that robots will have the potential to facilitate children’s learning and function autonomously within real classrooms in the near future. Previous research has raised the importance of designing acceptable robots for different practices. In parallel, scholars have raised ethical concerns surrounding children interacting with robots. Drawing on a Responsible Research and Innovation perspective, our goal is to move away from research concerned with designing features that will render robots more socially acceptable by end users toward a reflective dialogue whose goal is to consider the key ethical issues and long-term consequences of implementing classroom robots for teachers and children in primary education. This paper presents the results from several focus groups conducted with teachers in three European countries. Through a thematic analysis, we provide a theoretical account of teachers’ perspectives on classroom robots pertaining to privacy, robot role, effects on children and responsibility. Implications for the field of educational robotics are discussed.

Just follow the suit! Trust in human-robot interactions during card game playing

Robots are currently being developed to enter our lives and interact with us in different tasks. For humans to be able to have a positive experience of interaction with such robots, they need to trust them to some degree. In this paper, we present the development and evaluation of a social robot that was created to play a card game with humans, playing the role of a partner and opponent. This type of activity is especially important, since our target group is elderly people - a population that often suffers from social isolation. Moreover, the card game scenario can lead to the development of interesting trust dynamics during the interaction, in which the human that partners with the robot needs to trust it in order to succeed and win the game. The design of the robots behavior and game dynamics was inspired in previous user-centered design studies in which elderly people played the same game. Our evaluation results show that the levels of trust differ according to the previous knowledge that players have of their partners. Thus, humans seem to significantly increase their trust level towards a robot they already know, whilst maintaining the same level of trust in a human that they also previously knew. Henceforth, this paper shows that trust is a multifaceted construct that develops differently for humans and robots.

Discovering Social Interaction Strategies for Robots from Restricted-Perception Wizard-of-Oz Studies

In this paper we propose a methodology for the creation of social interaction strategies for human-robot interaction based on restricted-perception Wizard-of-Oz studies (WoZ). This novel experimental technique involves restricting the wizards perceptions over the environment and the behaviors it controls according to the robots inherent perceptual and acting limitations. Within our methodology, the robots design lifecycle is divided into three consecutive phases, namely data collection, where we perform interaction studies to extract expert knowledge and interaction data; strategy extraction, where a hybrid strategy controller for the robot is learned based on the gathered data; strategy refinement, where the controller is iteratively evaluated and adjusted. We developed a fully-autonomous robotic tutor based on the proposed approach in the context of a collaborative learning scenario. The results of the evaluation study show that, by performing restricted-perception WoZ studies, our robots are able to engage in very natural and socially-aware interactions.

More Social and Emotional Behaviour May Lead to Poorer Perceptions of a Social Robot

In this paper we present a study with an autonomous robot that plays a game against a participant, while expressing some social behaviors. We tried to explore the role of emotional sharing from the robot to the user, in order to understand how it might affect the perception of the robot by its users. To study this, two different conditions were formulated: 1-Sharing Condition (the robot shared its emotional state at the end of each board game); and 2-No Sharing Condition (the robot did not shared its emotions). Participants were randomly assigned to one of the conditions and this study followed a between-subject design methodology. It was expected that in the Sharing Condition participants would feel closer to the robot and would perceive/evaluate it as more humanlike. But results contradicted this expectation and called our attention for the caution that needs to exist when building social behaviours to implement in human-robot interactions (HRI).

An Empathic Robotic Tutor for School Classrooms: Considering Expectation and Satisfaction of Children as End-Users

Before interacting with a futuristic technology such as a robot, there is a lot of space for the creation of a whole set of expectations towards that interaction. Once that interaction happens, users can be left with a hand full of satisfaction, dissatisfaction, or even a mix of both. To study the possible role of experience as a mediator between expectation and satisfaction, we developed a scale for HRI that measures expectations and satisfaction of the users. Afterwards, we conducted a study with end-users interacting with a social robot. The robot is being developed to be an empathic robotic tutor to be used in real schools, with input from primary end-users (children). Children’s expectations and subsequent satisfaction after the interaction with the robotic tutor were analysed. The results can be fed back to the system developers on how well it is being designed for such a target population, and what factors regarding their expectation and satisfaction have shifted after the experience of interaction. By delivering on the children’s expectations, we aim to design a robotic tutor that provides enough satisfaction to sustain an enjoyable and natural interaction in the real educational environment.

A Study on Trust in a Robotic Suitcase

This work presents a study on human-robot interaction between a prototype of a robotic suitcase – aBag – and people using it. Importantly, for an autonomous robotic suitcase to be successful as a product, people need to trust it. Therefore, a study was performed, where participants used aBag (remotely operated using the Wizard of Oz technique) for carrying their belongings. Two different conditions were created: (1) aBag follows the participant at a close range; (2) aBag follows the participant on a further distance. We expected that participants would trust more aBag when it was following them at a close range, but interestingly participants seemed to trust more when aBag was further away. Also, regardless of the conditions, the level of trust in aBag was significantly higher after the interaction compared to before, bringing positive results to the development of this kind of robotic apparatus.

Groups of humans and robots: Understanding membership preferences and team formation

Although groups of robots are expected to interact with groups of humans in the near future, research related to teams of humans and robots still appears scarce. This paper contributes to the study of human-robot teams by investigating how humans choose robots to partner with in a multi-party game context. The novelty of our work concerns the successful design and development of two social robots that are able to autonomously interact with a group of two humans in the execution of a social and entertaining task. The development of these two characters was motivated by psychological research on learning goal theory, according to which we interpret and approach a given task differently depending on our learning goal (oriented more towards either relationship building or competition). Thus, we developed two robotic characters implemented in two robots: Emys (competitive robot) and Glin (relationship-driven robot). In our study, a group of four (two humans and two autonomous robots) engaged in a social and entertaining card game. Our study yields several important conclusions regarding groups of humans and robots. (1) When a partner is chosen without previous partnering experience, people tend to prefer robots with relationship-driven characteristics as their partners compared with competitive robots. (2) After some partnering experience has been gained, the choice becomes less clear and additional driving factors emerge: (2a) participants with higher levels of competitiveness (personal characteristics) tend to prefer Emys, whereas those with lower levels prefer Glin, and (2b) the choice of which robot to partner with also depends on team performance, with the winning team being the preferred choice.

Emotional sharing behavior for a social robot in a competitive setting

Is a robot that shares explicitly its emotions with users more believable and friendly? In a previous study addressing this question, results suggested that an emotion sharing feature in a robot may have negative effects in the perception of that robot. Here, we address the same question but also take into account the “competence” of a robot executing a task, to understand if some kind of interaction occurs.

The Empathic Robotic Tutor: Featuring the NAO Robot

We present an autonomous empathic robotic tutor to be used in classrooms as a peer in a virtual learning environment. The system merges a virtual agent design with HRI features, consisting of a robotic embodiment, a multimedia interactive learning application and perception sensors that are controlled by an artificial intelligence agent.