Empathic Robot for Group Learning

Is it possible for people to develop a sense of empathy toward humanoid robots and establish meaningful relationships with them?

Introduction

Empathy can be described as the ability to adopt another person's perspective and comprehend, feel, share, and respond to their emotional experiences. Empathy plays an important role in these relationships and is constructed in human-robot interaction (HRI). This systematic review focuses on studies investigating human empathy toward robots. We intend to define empathy as the cognitive capacity of humans to perceive robots as equipped with emotional and psychological states.

Methods

We conducted a systematic search of peer-reviewed articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched Scopus, PubMed, Web of Science, and Embase databases. All articles were reviewed based on the titles, abstracts, and full texts by two investigators (EM and CS) who independently performed data collection. The researchers read the full-text articles deemed suitable for the study, and in cases of disagreement regarding the inclusion and exclusion criteria, the final decision was made by a third researcher (VLB).

Results

The electronic search identified 484 articles. After reading the full texts of the selected publications and applying the predefined inclusion criteria, we selected 11 articles that met our inclusion criteria. Robots that could identify and respond appropriately to the emotional states of humans seemed to evoke empathy. In addition, empathy tended to grow more when the robots exhibited anthropomorphic traits.

Discussion

Humanoid robots can be programmed to understand and react to human emotions and simulate empathetic responses; however, they are not endowed with the same innate capacity for empathy as humans.

When a Robot Is Your Teammate

Creating effective teamwork between humans and robots involves not only addressing their performance as a team but also sustaining the quality and sense of unity among teammates, also known as cohesion. This paper explores the research problem of: how can we endow robotic teammates with social capabilities to improve the cohesive alliance with humans? By defining the concept of a human-robot cohesive alliance in the light of the multidimensional construct of cohesion from the social sciences, we propose to address this problem through the idea of multifaceted human-robot cohesion. We present our preliminary effort from previous works to examine each of the five dimensions of cohesion: social, collective, emotional, structural, and task. We finish the paper with a discussion on how human-robot cohesion contributes to the key questions and ongoing challenges of creating robotic teammates. Overall, cohesion in human-robot teams might be a key factor to propel team performance and it should be considered in the design, development, and evaluation of robotic teammates.

Artificial Emotional Intelligence in Socially Assistive Robots for Older Adults: A Pilot Study

This paper presents our recent research on integrating artificial emotional intelligence in a social robot (Ryan) and studies the robot's effectiveness in engaging older adults. Ryan is a socially assistive robot designed to provide companionship for older adults with depression and dementia through conversation. We used two versions of Ryan for our study, empathic and non-empathic. The empathic Ryan utilizes a multimodal emotion recognition algorithm and a multimodal emotion expression system. Using different input modalities for emotion, i.e. facial expression and speech sentiment, the empathic Ryan detects users emotional state and utilizes an affective dialogue manager to generate a response. On the other hand, the non-empathic Ryan lacks facial expression and uses scripted dialogues that do not factor in the users emotional state. We studied these two versions of Ryan with 10 older adults living in a senior care facility. The statistically significant improvement in the users' reported face-scale mood measurement indicates an overall positive effect from the interaction with both the empathic and non-empathic versions of Ryan. However, the number of spoken words measurement and the exit survey analysis suggest that the users perceive the empathic Ryan as more engaging and likable.

Feeling with a robot-the role of anthropomorphism by design and the tendency to anthropomorphize in human-robot interaction

The implementation of anthropomorphic features in regard to appearance and framing is widely supposed to increase empathy towards robots. However, recent research used mainly tasks that are rather atypical for daily human-robot interactions like sacrificing or destroying robots. The scope of the current study was to investigate the influence of anthropomorphism by design on empathy and empathic behavior in a more realistic, collaborative scenario. In this online experiment, participants collaborated either with an anthropomorphic or a technical looking robot and received either an anthropomorphic or a technical description of the respective robot. After the task completion, we investigated situational empathy by displaying a choice-scenario in which participants needed to decide whether they wanted to act empathically towards the robot (sign a petition or a guestbook for the robot) or non empathically (leave the experiment). Subsequently, the perception of and empathy towards the robot was assessed. The results revealed no significant influence of anthropomorphism on empathy and participants' empathic behavior. However, an exploratory follow-up analysis indicates that the individual tendency to anthropomorphize might be crucial for empathy. This result strongly supports the importance to consider individual difference in human-robot interaction. Based on the exploratory analysis, we propose six items to be further investigated as empathy questionnaire in HRI.

Bonding with a Couchsurfing Robot: The Impact of Common Locus on Human-Robot Bonding In-the-wild

Due to an increased presence of robots in human-inhabited environments, we observe a growing body of examples in which humans show behavior that is indicative of strong social engagement towards robots that do not possess any life-like realism in appearance or behavior. In response, we focus on the under-explored concept of a common locus as a relevant driver for a robot passing a social threshold. The key principle of common locus is that sharing place and time with a robotic artifact functions as an important catalyst for a perception of shared experiences, which in turn leads to bonding. We present BlockBots, minimal cube-shaped robotic artifacts that are deployed in an unsupervised, open-ended and in-the-field experimental setting aimed to explore the relevance of this concept. Participants host the BlockBot in their domestic environment before passing it on, without necessarily knowing they are taking part in an experiment. Qualitative data suggest that participants make identity and mind attributions to the BlockBot. People that actively maintain a common locus with BlockBot by taking it with them when changing location, on trips and during outdoor activities, project more of these attributes than others.

The Effects of Healthcare Robot Empathy Statements and Head Nodding on Trust and Satisfaction: A Video Study

Clinical empathy has been associated with many positive outcomes, including patient trust and satisfaction. Physicians can demonstrate clinical empathy through verbal statements and non-verbal behaviors, such as head nodding. The use of verbal and non-verbal empathy behaviors by healthcare robots may also positively affect patient outcomes. The current study examined whether the use of robot verbal empathy statements and head nodding during a video recorded interaction between a healthcare robot and patient improved participant trust and satisfaction. One hundred participants took part in the experiment, online through Amazon Mechanical Turk. They were randomized to watch an interaction with a Nao robot that (1) either made empathetic or neutral statements and (2) nodded its head when listening to the patient or not. Results showed that the use of empathetic statements by the healthcare robot significantly increased participant perceptions of robot empathy, trust and satisfaction and reduced robot distrust. No significant findings were revealed in relation to robot head nodding. The positive effects of empathy statements support the model of Robot-Patient Communication, which theorizes that robot use of recommended clinical empathy behaviors can improve patient outcomes. The effects of healthcare robot nodding behavior need to be further investigated.

Socially Assistive Robots as Storytellers That Elicit Empathy

Empathy is the ability to share someone else’s feelings or experiences; it influences how people interact and relate. Socially assistive robots (SAR) are a promising means of conveying and eliciting empathy toward facilitating human-robot interaction. This work examines factors that influence the amount of empathy elicited by a SAR storyteller and users’ perceptions of that robot. We conducted an empirical mixed-design study (N=46) using an autonomous SAR storyteller that told three stories, each with a different human or robot target of empathy. The robot storyteller used the first-person narrative voice (1PNV) with half of the participants and the third-person narrative voice (3PNV) with the other half. We found that the SAR storyteller elicited significantly more empathy when the story target of empathy matched the SAR narrator, i.e., was also a robot. Additionally, the 1PNV robot elicited significantly more empathy and was perceived as more human-like, easy to interact with, and trustworthy than the 3PNV robot. Finally, participants who empathized more with the robot displayed facial expressions consistent with the emotional story content. These insights inform the design of SAR storytellers capable of eliciting empathy toward creating compelling and effective human-robot interactions.

Robot-Enhanced Language Learning for Children in Norwegian Day-Care Centers

In a case study, we transformed the existing learning program Language Shower, which is used in some Norwegian day-care centers in the Grorud district of Oslo municipality, into a digital solution using an app for smartphones or tablets with the option for further enhancement of the presentation by a NAO robot. The solution was tested in several iterations and multiple day-care centers over several weeks. Measurements of the children’s progress across learning sessions indicated a positive impact of the program using a robot as compared to the program without a robot. In situ observations and interviews with day-care center staff confirmed the solution’s many advantages, but also revealed some important areas for improvement. In particular, the speech recognition needs to be more flexible and robust, and special measures have to be in place to handle children speaking simultaneously.

Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review

Research on affective communication for socially assistive robots has been conducted to enable physical robots to perceive, express, and respond emotionally. However, the use of affective computing in social robots has been limited, especially when social robots are designed for children, and especially those with autism spectrum disorder (ASD). Social robots are based on cognitive-affective models, which allow them to communicate with people following social behaviors and rules. However, interactions between a child and a robot may change or be different compared to those with an adult or when the child has an emotional deficit. In this study, we systematically reviewed studies related to computational models of emotions for children with ASD. We used the Scopus, WoS, Springer, and IEEE-Xplore databases to answer different research questions related to the definition, interaction, and design of computational models supported by theoretical psychology approaches from 1997 to 2021. Our review found 46 articles; not all the studies considered children or those with ASD.

Trouble and Repair in Child-Robot Interaction: A Study of Complex Interactions With a Robot Tutee in a Primary School Classroom

Today, robots are studied and expected to be used in a range of social roles within classrooms. Yet, due to a number of limitations in social robots, robot interactions should be expected to occasionally suffer from troublesome situations and breakdowns. In this paper, we explore this issue by studying how children handle interaction trouble with a robot tutee in a classroom setting. The findings have implications not only for the design of robots, but also for evaluating their benefit in, and for, educational contexts. In this study, we conducted video analysis of children's group interactions with a robot tutee in a classroom setting, in order to explore the nature of these troubles in the wild. Within each group, children took turns acting as the primary interaction partner for the robot within the context of a mathematics game. Specifically, we examined what types of situations constitute trouble in these child-robot interactions, the strategies that individual children employ to cope with this trouble, as well as the strategies employed by other actors witnessing the trouble. By means of Interaction Analysis, we studied the video recordings of nine group interaction sessions (n = 33 children) in primary school grades 2 and 4. We found that sources of trouble related to the robot's social norm violations, which could be either active or passive. In terms of strategies, the children either persisted in their attempts at interacting with the robot by adapting their behavior in different ways, distanced themselves from the robot, or sought the help of present adults (i.e., a researcher in a teacher role, or an experimenter) or their peers (i.e., the child's classmates in each group). In terms of the witnessing actors, they addressed the trouble by providing guidance directed at the child interacting with the robot, or by intervening in the interaction. These findings reveal the unspoken rules by which children orient toward social robots, the complexities of child-robot interaction in the wild, and provide insights on children's perspectives and expectations of social robots in classroom contexts.

I–C–E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction

The research community of human-robot interaction relies on theories and phenomena from the social sciences in order to study and validate robotic developments in interaction. These studies mainly concerned one (human) on one (robot) interactions in the past. The present paper shifts the attention to groups and group dynamics and reviews relevant concepts from the social sciences: ingroup identification (I), cohesion (C) and entitativity (E). Ubiquitous robots will be part of larger social settings in the near future. A conceptual framework, the I–C–E framework, is proposed as a theoretical foundation for group (dynamics) research in HRI. Additionally, we present methods and possible measures for these relevant concepts and outline topics for future research.

Robot-Supported Collaborative Learning (RSCL): Social Robots as Teaching Assistants for Higher Education Small Group Facilitation

Acknowledging the benefits of active learning and the importance of collaboration skills, the higher education system has started to transform toward utilization of group activities into lecture hall culture. In this study, a novel interaction has been introduced, wherein a social robot facilitated a small collaborative group activity of students in higher education. Thirty-six students completed a 3 h activity that covered the main content of a course in Human Computer Interaction. In this within-subject study, the students worked in groups of four on three activities, moving between three conditions: instructor facilitation of several groups using pen and paper for the activity; tablets facilitation, also used for the activity; and robot facilitation, using tablets for the activity. The robot facilitated the activity by introducing the different tasks, ensuring proper time management, and encouraging discussion among the students. This study examined the effects of facilitation type on attitudes toward the activity facilitation, the group activity, and the robot, using quantitative, and qualitative measures. Overall students perceived the robot positively, as friendly and responsive, even though the robot did not directly respond to the students' verbal communications. While most survey items did not convey significant differences between the robot, tablet, or instructor, we found significant correlations between perceptions of the robot, and attitudes toward the activity facilitation, and the group activity. Qualitative data revealed the drawbacks and benefits of the robot, as well as its relative perceived advantages over a human facilitator, such as better time management, objectivity, and efficiency. These results suggest that the robot's complementary characteristics enable a higher quality learning environment, that corresponds with students' requirements and that a Robot Supportive Collaborative Learning (RSCL) is a promising novel paradigm for higher education.

Human Group Presence, Group Characteristics, and Group Norms Affect Human-Robot Interaction in Naturalistic Settings

As robots become more prevalent in public spaces, such as museums, malls, and schools, they are coming into increasing contact with groups of people, rather than just individuals. Groups, compared to individuals, can differ in robot acceptance based on the mere presence of a group, group characteristics such as entitativity (i.e., cohesiveness), and group social norms; however, group dynamics are seldom studied in relation to robots in naturalistic settings. To examine how these factors affect human-robot interaction, we observed 2,714 people in a Japanese mall receiving directions from the humanoid robot Robovie. Video and survey responses evaluating the interaction indicate that groups, especially entitative groups, interacted more often, for longer, and more positively with the robot than individuals. Participants also followed the social norms of the groups they were part of; participants who would not be expected to interact with the robot based on their individual characteristics were more likely to interact with it if other members of their group did. These results illustrate the importance of taking into account the presence of a group, group characteristics, and group norms when designing robots for successful interactions in naturalistic settings.