An emotion and memory model for social robots : a long-term interaction

In this thesis, we investigate the role of emotions and memory in social robotic companions. In particular, our aim is to study the effect of an emotion and memory model towards sustaining engagement and promoting learning in a long-term interaction. Our Emotion and Memory model was based on how humans create memory under various emotional events/states. The model enabled the robot to create a memory account of user's emotional events during a long-term child-robot interaction. The robot later adapted its behaviour through employing the developed memory in the following interactions with the users. The model also had an autonomous decision-making mechanism based on reinforcement learning to select behaviour according to the user preference measured through user's engagement and learning during the task. The model was implemented on the NAO robot in two different educational setups. Firstly, to promote user's vocabulary learning and secondly, to inform how to calculate area and perimeter of regular and irregular shapes. We also conducted multiple long-term evaluations of our model with children at the primary schools to verify its impact on their social engagement and learning. Our results showed that the behaviour generated based on our model was able to sustain social engagement. Additionally, it also helped children to improve their learning. Overall, the results highlighted the benefits of incorporating memory during child-Robot Interaction for extended periods of time. It promoted personalisation and reflected towards creating a child-robot social relationship in a long-term interaction

Emotion and memory model for social robots: a reinforcement learning based behaviour selection

In this paper, we propose a reinforcement learning (RL) mechanism for social robots to select an action based on users’ learning performance and social engagement. We applied this behavior selection mechanism to extend the emotion and memory model, which allows a robot to create a memory account of the user’s emotional events and adapt its behavior based on the developed memory. We evaluated the model in a vocabulary-learning task at a school during a children’s game involving robot interaction to see if the model results in maintaining engagement and improving vocabulary learning across the four different interaction sessions. Generally, we observed positive findings based on child vocabulary learning and sustaining social engagement during all sessions. Compared to the trends of a previous study, we observed a higher level of social engagement across sessions in terms of the duration of the user gaze toward the robot. For vocabulary retention, we saw similar trends in general but also showing high vocabulary retention across some sessions. The findings indicate the benefits of applying RL techniques that have a reward system based on multi-modal user signals or cues

Applying adaptive social mobile agent to facilitate learning

In this paper, we present our idea about applying an adaptive social mobile agent in a game based scenario to support foreign language vocabulary learning. We hypothesize that through implementing an adaptive agent, we may mitigate the problem of a loss in child engagement or may also prolong the time a child takes to lose interest. We then present details on architecture and implementation of an adaptive social mobile agent

Towards the Applicability of NAO Robot for Children with Autism in Pakistan

In this paper, we present a HRI study that reports on the potential of NAO as a socially assistive robot in Pakistan. Our findings generated through interviewing 2 parents and 5 teachers on the plausibility of using NAO robot as an interaction partner show that both groups welcomed the use of NAO at schools. They, however, were sceptical due to missing NAO’s facial expressions and certain body parts such as nose and lips. They also emphasised the importance of creating natural text to speech interface for the Urdu Language. Our findings taken from 7 autistic children to measure their level of social interaction during HRI revealed that children positively engaged with the NAO robot and showed a significant number of both verbal and non-verbal behaviours

Towards the applicability of the social robot in the role of an invigilator

We see three different roles (peer, tutee, tutor) for humanoid robots in education. The role of a monitor or an invigilator is understudied. Therefore, in this paper, we describe a method for the social robot to act as an invigilator. We implemented this method on the Nao robot. We conducted a study to compare an active invigilator robot v.s. a passive invigilator robot. The study aimed to understand the participant's perception of the robot in the role of an invigilator. We found that the robot was highly rated as an invigilator regardless of the condition. Participants were generally satisfied with the use of the robot as an invigilator. Participants rated the active robot invigilator to be more human-like and responsive as compared to the passive one. The findings highlight the applicability of the role of a robot invigilator in education and call for a more careful investigation in the future

Exploring the potential of NAO robot as an interviewer

In this paper, we present our early findings on the utilization of a social robot during the formal interview process. We implemented a mechanism that enabled the robot to ask context-aware questions based on the data based on the resume or linked-in profile of the applicant. Later, we conducted an exploratory between-subject evaluation with 8 adult participants to find the difference in the duration of applicants responses given to the NAO robot and to the human interviewer. Our results didn’t find the significant difference in terms of participant responses to human and robotic interviewers

HCI down under: reflecting on a decade of the OzCHI conference

In this study we present a scientometric analysis of the Australian Conference on Human–Computer Interaction (OzCHI) proceedings over the period of a decade (2006–2015). Conference proceedings were manually extracted from the ACM Digital Library and analysed. We observed OzCHI to be a popular conference attracting both submissions and citations. A group of leading researchers dominated the publication count followed by a long list of mid career academics. We observed the themes of Design, Health and Well-being and Education to be growing in importance. We also observed that full papers were cited significantly more than short papers. We conclude with a reflection on our methodology and a proposal of recommendations for the HCI/OzCHI community in Australia

Understanding Behaviours and Roles for Social and Adaptive Robots In Education

In order to establish a long-term relationship between a robot and a child, robots need to learn from the environment, adapt to specific user needs and display behaviours and roles accordingly. Literature shows that certain robot behaviours could negatively impact child’s learning and performance [17]. Therefore, the purpose of the present study is to not only understand teacher’s opinion on the existing effective social behaviours and roles but also to understand novel behaviours that can positively influence children performance in a language learning setting. In this paper, we present our results based on interviews conducted with 8 language teachers to get their opinion on how a robot can efficiently perform behaviour adaptation to influence learning and achieve long term engagement. We also present results on future directions extracted from the interviews with teachers

Adaptive social robot for sustaining social engagement during long-term children-robot interaction

One of the known challenges in Children Robot Interaction (cHRI) is to sustain children’s engagement during long-term interactions with robots. Researchers have hypothesised that robots that can adapt to children’s affective states, and can also learn from the environment, can result in sustaining engagement during cHRI. Recently, researchers have conducted a range of studies where robots portray different social capabilities and have shown that it has positively influenced children’s engagement. However, despite an immense body of research on implementation of different adaptive social robots, a pivotal question that remains unanswered: Which adaptations portrayed by a robot can result in maintaining long-term social engagement during cHRI. In other words, what are the appropriate and effective adaptations portrayed by a robot that will sustain social engagement for an extended number of interactions? In this paper, we report on a study conducted with three groups of children who played a snakes and ladders game with the NAO robot to address the aforementioned question. The NAO performed 1) Game based adaptations, 2) Emotion based adaptations and 3) Memory-based adaptation. Our results showed that emotion based adaptation was found out to be most effective followed by memory based adaptation. Game adaptation didn’t result in sustaining long-term social engagement

Children views' on social robot's adaptations in education

In this paper, we present our results of a long-term study conducted at a school with 12 children, in which they played snakes and ladders game with a NAO robot for 3- times across 10 days. The goal of the study was to understand children's view on various adaptation behaviours such as emotion, memory, personality for a social robot in education for maintaining and creating long-term engagement and acceptance. On the last day, we divided the children into 4 different groups to perform a focus group activity with them. Our results show that children reacted positively towards the use of robots in education. Children also emphasised that the robot should adapt based on previous memory, their emotions, and personality in real-time