74 research outputs found

    Socially assistive robots : the specific case of the NAO

    Get PDF
    Numerous researches have studied the development of robotics, especially socially assistive robots (SAR), including the NAO robot. This small humanoid robot has a great potential in social assistance. The NAO robot’s features and capabilities, such as motricity, functionality, and affective capacities, have been studied in various contexts. The principal aim of this study is to gather every research that has been done using this robot to see how the NAO can be used and what could be its potential as a SAR. Articles using the NAO in any situation were found searching PSYCHINFO, Computer and Applied Sciences Complete and ACM Digital Library databases. The main inclusion criterion was that studies had to use the NAO robot. Studies comparing it with other robots or intervention programs were also included. Articles about technical improvements were excluded since they did not involve concrete utilisation of the NAO. Also, duplicates and articles with an important lack of information on sample were excluded. A total of 51 publications (1895 participants) were included in the review. Six categories were defined: social interactions, affectivity, intervention, assisted teaching, mild cognitive impairment/dementia, and autism/intellectual disability. A great majority of the findings are positive concerning the NAO robot. Its multimodality makes it a SAR with potential

    Sign Language Representation by TEO Humanoid Robot: End-User Interest, Comprehension and Satisfaction

    Get PDF
    In this paper, we illustrate our work on improving the accessibility of Cyber&-Physical Systems (CPS), presenting a study on human&-robot interaction where the end-users are either deaf or hearing-impaired people. Current trends in robotic designs include devices with robotic arms and hands capable of performing manipulation and grasping tasks. This paper focuses on how these devices can be used for a different purpose, which is that of enabling robotic communication via sign language. For the study, several tests and questionnaires are run to check and measure how end-users feel about interpreting sign language represented by a humanoid robotic assistant as opposed to subtitles on a screen. Stemming from this dichotomy, dactylology, basic vocabulary representation and end-user satisfaction are the main topics covered by a delivered form, in which additional commentaries are valued and taken into consideration for further decision taking regarding robot-human interaction. The experiments were performed using TEO, a household companion humanoid robot developed at the University Carlos III de Madrid (UC3M), via representations in Spanish Sign Language (LSE), and a total of 16 deaf and hearing-impaired participants.The research leading to these results has received funding from the RoboCity2030-III-CM project (RobĂłtica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

    User Experience Design and Evaluation of Persuasive Social Robot As Language Tutor At University : Design And Learning Experiences From Design Research

    Get PDF
    Human Robot Interaction (HRI) is a developing field where research and innovation are progressing. One domain where Human Robot Interaction has focused is in the educational sector. Various research has been conducted in education field to design social robots with appropriate design guidelines derived from user preferences, context, and technology to help students and teachers to foster their learning and teaching experience. Language learning has become popular in education due to students receiving opportunities to study and learn any interested subjects in any language in their preferred universities around the world. Thus, being the reason behind the research of using social robots in language learning and teaching in education field. To this context this thesis explored the design of language tutoring robot for students learning Finnish language at university. In language learning, motivation, the learning experience, context, and user preferences are important to be considered. This thesis focuses on the Finnish language learning students through language tutoring social robot at Tampere University. The design research methodology is used to design the persuasive language tutoring social robot teaching Finnish language to the international students at Tampere University. The design guidelines and the future language tutoring robot design with their benefits are formed using Design Research methodology. Elias Robot, a language tutoring application designed by Curious Technologies, Finnish EdTech company was used in the explorative user study. The user study involved Pepper, Social robot along with the Elias robot application using Mobile device technology. The user study was conducted in university, the students include three male participants and four female participants. The aim of the study was to gather the design requirements based on learning experiences from social robot tutor. Based on this study findings and the design research findings, the future language tutoring social robot was co-created through co design workshop. Based on the findings from Field study, user study, technology acceptance model findings, design research findings, student interviews, the persuasive social robot language tutor was designed. The findings revealed all the multi modalities are required for the efficient tutoring of persuasive social robots and the social robots persuade motivation with students to learn the language. The design implications were discussed, and the design of social robot tutor are created through design scenarios

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

    Get PDF
    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

    Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

    Get PDF
    Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD
    • …
    corecore