In this paper we introduce the core results of the project on software development for social robotics systems. The usability of maintenance and control features is crucial for many kinds of systems, but in the case of social robotics we also have to take into account that (1) the humanoid robot physically interacts with humans, (2) the conversation with children might have different requirements in comparison to the conversation with adults. The results of our work were implement for the humanoid PAL REEM robot, but their core ideas can be applied for other types of humanoid robots. We developed a web-based solution that supports the management of robot-guided tours, provides recommendations for the users as well as allows for a visual analysis of the data on previous tours.
There were also a number of surveys and literature reviews on the related topics. A survey on social robots for long-term interaction was presented in @cite_2 . A systematic review on application of social robotics in the Autism Spectrum Disorders treatment was presented in @cite_6 . @cite_1 presented a survey on the roles and benefits of social robots in the therapy of children with autism.
@cite_1 @cite_6 @cite_2
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Social robotics could be a promising method for Autism Spectrum Disorders (ASD) treatment. The aim of this article is to carry out a systematic literature review of the studies on this topic that were published in the last 10 years. We tried to address the following questions: can social robots be a useful tool in autism therapy? We followed the PRISMA guidelines, and the protocol was registered within PROSPERO database (CRD42015016158). We found many positive implications in the use of social robots in therapy as for example: ASD subjects often performed better with a robot partner rather than a human partner; sometimes, ASD patients had, toward robots, behaviors that TD patients had toward human agents; ASDs had a lot of social behaviors toward robots; during robotic sessions, ASDs showed reduced repetitive and stereotyped behaviors and, social robots manage to improve spontaneous language during therapy sessions. Therefore, robots provide therapists and researchers a means to connect with autistic subjects in an easier way, but studies in this area are still insufficient. It is necessary to clarify whether sex, intelligence quotient, and age of participants affect the outcome of therapy and whether any beneficial effects only occur during the robotic session or if they are still observable outside the clinical experimental context. Autism Res 2016, 9: 165–183. © 2015 International Society for Autism Research, Wiley Periodicals, Inc. Investigation into robot-assisted intervention for children with autism spectrum disorder (ASD) has gained momentum in recent years. Therapists involved in interventions must overcome the communication impairments generally exhibited by children with ASD by adeptly inferring the affective cues of the children to adjust the intervention accordingly. Similarly, a robot must also be able to understand the affective needs of these children-an ability that the current robot-assisted ASD intervention systems lack-to achieve effective interaction that addresses the role of affective states in human-robot interaction and intervention practice. In this paper, we present a physiology-based affect-inference mechanism for robot-assisted intervention where the robot can detect the affective states of a child with ASD as discerned by a therapist and adapt its behaviors accordingly. This paper is the first step toward developing ldquounderstandingrdquo robots for use in future ASD intervention. Experimental results with six children with ASD from a proof-of-concept experiment (i.e., a robot-based basketball game) are presented. The robot learned the individual liking level of each child with regard to the game configuration and selected appropriate behaviors to present the task at his her preferred liking level. Results show that the robot automatically predicted individual liking level in real time with 81.1 accuracy. This is the first time, to our knowledge, that the affective states of children with ASD have been detected via a physiology-based affect recognition technique in real time. This is also the first time that the impact of affect-sensitive closed-loop interaction between a robot and a child with ASD has been demonstrated experimentally. As a great number of robotic products are entering people’s lives, the question of how can they behave in order to sustain long-term interactions with users becomes increasingly more relevant. In this paper, we present an empathic model for social robots that aim to interact with children for extended periods of time. The application of this model to a scenario where a social robot plays chess with children is described. To evaluate the proposed model, we conducted a long-term study in an elementary school and measured children’s perception of social presence, engagement and social support. For robots to interact effectively with human users they must be capable of coordinated, timely behavior in response to social context. The Adaptive Strategies for Sustainable Long-Term Social Interaction (ALIZ-E) project focuses on the design of long-term, adaptive social interaction between robots and child users in real-world settings. In this paper, we report on the iterative approach taken to scientific and technical developments toward this goal: advancing individual technical competencies and integrating them to form an autonomous robotic system for evaluation "in the wild." The first evaluation iterations have shown the potential of this methodology in terms of adaptation of the robot to the interactant and the resulting influences on engagement. This sets the foundation for an ongoing research program that seeks to develop technologies for social robot companions. Here, we propose that bidirectionality in implicit motor coordination between humanoid robots and humans could enhance the social competence of human–robot interactions. We first detail some questions pertaining to human–robot interactions, introducing the Uncanny Valley hypothesis. After introducing a framework pertinent for the understanding of natural social interactions, motor resonance, we examine two behaviors derived from this framework: motor coordination, investigated in and informative about human–human interaction, and motor interference, which demonstrate the relevance of the motor resonance framework to describe human perception of humanoid robots. These two lines of investigation are then put together to “close the loop” by proposing to implement a key feature of motor coordination, bidirectionality, in robots’ behavior. Finally, we discuss the feasibility of implementing motor coordination between humanoid robots and humans, and the consequences of this implementation in enhancing the social competence of robots interacting with humans. Keywords: interpersonal interaction, motor resonance, motor coordination, motor interference, social robotics, anthropomorphism In this paper we present the results of a qualitative study with therapists to inform social robotics and human robot interaction (HRI) for engagement in rehabilitative therapies. Our results add to growing evidence that socially assistive robots (SARs) could play a role in addressing patients' low engagement with self-directed exercise programmes. Specifically, we propose how SARs might augment or offer more pro-active assistance over existing technologies such as smartphone applications, computer software and fitness trackers also designed to tackle this issue. In addition, we present a series of design implications for such SARs based on therapists' expert knowledge and best practices extracted from our results. This includes an initial set of SAR requirements and key considerations concerning personalised and adaptive interaction strategies. We introduce a new problem domain for activity recognition: the analysis of children's social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3-5 minute child-adult interaction. In each session, the adult examiner followed a semi-structured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe methods for decoding the interactions. We present experimental results that demonstrate the potential of the dataset to drive interesting research questions, and show preliminary results for multi-modal activity recognition. Robot-therapy with emotional robots is a new approach for people with cognitive impairments. This article looks at explorative findings of the therapeutic seal PARO in a practice network in England and findings of PARO and the toy dinosaur PLEO in teaching research projects in Germany. The findings in the UK and Germany have comparable outcomes on social interaction of the residents. Examples of the outcomes are presented and analysed with respect to the question in how far there are indicators for a new quality of interaction due to the robotic intervention and in how far it can contribute to interventions which enhance social interaction in care. In this paper, we propose a “Response to Name Dataset” for autism spectrum disorder (ASD) study as well as a multimodal ASD auxiliary screening system based on machine learning. ASD children are characterized by their impaired interpersonal communication abilities and lack of response. In the proposed dataset, the reactions of children are recorded by cameras upon calling their names. The responsiveness of each child is then evaluated by a clinician with a score among 0, 1 and 2 following the Autism Diagnostic Observation Schedule (ADOS). We then develop a rule-based multimodal framework to quantitatively evaluate each child. Our system involves speech recognition based automatic name calling detection, face detection alignment, head pose estimation, and considers the response speed, eye contact duration and head orientation to output the final prediction. Compared to existing work, our dataset characterizes a more precise and detailed scoring system with clinical trial standards, as well as a more spontaneous setting by incorporating less lab-controlled sessions with dynamic cluttered environments, multi-pose mobile captured videos, and flexible number of accompanying adults. Experiments show that our machine predicted scores align closely with human professional diagnosis, showing promising potential in early screening of ASD, and shedding light on future clinical applications. Many activities of daily living, such as picking up glasses, holding a fork or opening a door, which most people do without thinking, can become insurmountable for people who have upper extremity disabilities. The alternative to asking for human help is to use some assistive devices to compensate their loss of mobility; however, many of those devices are limited in terms of functionality. Robotics may provide a better approach for the development of assistive devices, by allowing greater functionality. In this paper, we present results of a study (n=31) which objectives were to evaluate the efficacy of a new joystick-controlled seven-degree of freedom robotic manipulator and assess its potential economic benefits. Results show that JACO is easy to use as the majority of the participants were able to accomplish the testing tasks on their first attempt. The economic model results inferred that the use of the JACO arm system could potentially reduce caregiving time by 41 . These study results are expected to provide valuable data for interested parties, such as individuals with disabilities, their family or caregivers. We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism. The tasks present several challenges: a large dataset with long videos, a large number of highly variable actions, children that are only partially visible, have different ages and may show unpredictable behaviour, as well as non-standard camera viewpoints. We investigate how state-of-the-art 3d human pose reconstruction methods perform on the newly introduced tasks and propose extensions to adapt them to deal with these challenges. We also analyze multiple approaches in action and emotion recognition from 3d human pose data, establish several baselines, and discuss results and their implications in the context of child-robot interaction. Affect sensitivity is of the utmost importance for a robot companion to be able to display socially intelligent behaviour, a key requirement for sustaining long-term interactions with humans. This paper explores a naturalistic scenario in which children play chess with the iCat, a robot companion. A person-independent, Bayesian approach to detect the user's engagement with the iCat robot is presented. Our framework models both causes and effects of engagement: features related to the user's non-verbal behaviour, the task and the companion's affective reactions are identified to predict the children's level of engagement. An experiment was carried out to train and validate our model. Results show that our approach based on multimodal integration of task and social interaction-based features outperforms those based solely on non-verbal behaviour or contextual information (94.79 vs. 93.75 and 78.13 ). In contrast to conventional teaching agents (including robots) that were designed to play the role of human teachers or caregivers, we propose the opposite scenario in which robots receive instruction or care from children. We hypothesize that by using this care-receiving robot, we may construct a new educational framework whose goal is to promote children's spontaneous learning by teaching through their teaching the robot. In this paper, we describe the introduction of a care-receiving robot into a classroom at an English language school for Japanese children (3--6 years of age) and then conduct an experiment to evaluate if the care-receiving robot can promote their learning using English verbs. The results suggest that the idea of a care-receiving robot is feasible and that the robot can help children learn new English verbs efficiently. In addition, we report on investigations into several forms of teaching performed by children, which were revealed through observations of the children, parent interviews, and other useful knowledge. These can be used to improve the design of care-receiving robots for educational purposes. Virtual reality appears to be a promising and motivating platform to safely practice and rehearse social skills for children with Autism Spectrum Disorders (ASD). However, the literature to date is subject to limitations in elucidating the effectiveness of these virtual reality interventions. This study investigated the impact of a Virtual Reality Social Cognition Training to enhance social skills in children with ASD. Thirty children between the ages of 7-16 diagnosed with ASD completed 10, 1-h sessions across 5 weeks. Three primary domains were measured pre-post: emotion recognition, social attribution, attention and executive function. Results revealed improvements on measures of emotion recognition, social attribution, and executive function of analogical reasoning. These preliminary findings suggest that the use of a virtual reality platform offers an effective treatment option for improving social impairments commonly found in ASD. The VR-SCT provides an interactive and stimulating approach for children with ASD.The VR-SCT can improve social cognitive skills in children with ASD.Training improved emotion recognition, social attribution, and executive function.The training is a safe and socially non-threatening platform. This paper presents an overview of our work towards building socially intelligent, cooperative humanoid robots that can work and learn in partnership with people. People understand each other in social terms, allowing them to engage others in a variety of complex social interactions including communication, social learning, and cooperation. We present our theoretical framework that is a novel combination of Joint Intention Theory and Situated Learning Theory and demonstrate how this framework can be applied to develop our sociable humanoid robot, Leonardo. We demonstrate the robot's ability to learn quickly and effectively from natural human instruction using gesture and dialog, and then cooperate to perform a learned task jointly with a person. Such issues must be addressed to enable many new and exciting applications for robots that require them to play a long-term role in people's daily lives. Recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. This makes it increasingly difficult and inconvenient to program these tasks manually. Furthermore, humanoid robots, in contrast to industrial robots, should in the distant future behave within a social environment. Therefore, it must be possible to extend the robot's abilities in an easy and natural way. To address these requirements, this work investigates the topic of imitation learning of motor skills. The focus lies on providing a humanoid robot with the ability to learn new bi-manual tasks through the observation of object trajectories. For this, an imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models. The learned information is used to initialize an attractor-based movement generation algorithm that optimizes the reproduced movement towards the fulfillment of additional criteria, such as collision avoidance. Experiments performed with the humanoid robot ASIMO show that the proposed system is suitable for transferring information from a human demonstrator to the robot. These results provide a good starting point for more complex and interactive learning tasks. Introduction Animal-assisted therapy is gaining popularity as part of therapeutic activities for older adults in many long-term care facilities. However, concerns about dog bites, allergic responses to pets, disease, and insufficient available resources to care for a real pet have led to many residential care facilities to ban this therapy. There are situations where a substitute artificial companion, such as robotic pet, may serve as a better alternative. Methods This pilot study used a one-group pre- and posttest design to evaluate the effect of a robot-assisted therapy for older adults. Sixteen eligible participants participated in the study and received a group robot-assisted therapy using a seal-like robot pet for 30 minutes twice a week for 4 weeks. All participants received assessments of their communication and interaction skills using the Assessment of Communication and Interaction Skills (ACIS-C) and activity participation using the Activity Participation Scale at baseline and at week 4. Results A total of 12 participants completed the study. Wilcoxon signed rank test showed that participants' communication and interaction skills (z = −2.94, P = 0.003) and activity participation (z = −2.66, P = 0.008) were significantly improved after receiving 4-week robot-assisted therapy. Discussion By interacting with a robot pet, such as Paro, the communication, interaction skills, and activity participation of the older adults can be improved. The robot-assisted therapy can be provided as a routine activity program and has the potential to improve social health of older adults in residential care facilities. Social robots are emerging as potentially useful tools for customer service in various contexts including retail, healthcare or education. Their capacity for human-like interaction could provide a satisfactory customer experience in simple and repetitive tasks, while allowing human staff to focus on issues that are more complex. In this paper, we report the initial findings of a two-day field study of using a social robot Pepper for guidance and edutainment at the service point of a medium-sized city in Finland. We collected observations and semi-structured interviews of altogether 89 customers visiting the service point. Fifteen specific experiences evoked by the interaction with the robot were identified and categorized under five basic needs of Autonomy, Competence, Relatedness, Stimulation and Security. We discuss implications for an experience-driven design process of applications for social service robots. Even if a socially interactive robot has perfect information about the location, pose, and movement of humans in the environment, it is unclear how this information should be used to enable the initiation, maintenance, and termination of social interactions. We review models that have been developed to describe social engagement based on spatial relationships and describe a system developed for use on a robotic receptionist. The system uses spatial information from a laser tracker and head pose information from a camera to classify people in a categorical model of engagement. The robot's behaviors are determined by the presence of people in these different levels. We evaluate the system using observational behavioral analysis of recorded interactions between the robot and humans. This analysis suggests improvements to the current system: namely, to put a stronger emphasis on movement in the estimation of social engagement and to vary the timing of interactive behaviors The social difficulties that are a hallmark of autism spectrum disorder (ASD) are thought to arise, at least in part, from atypical attention toward stimuli and their features. To investigate this hypothesis comprehensively, we characterized 700 complex natural scene images with a novel three-layered saliency model that incorporated pixel-level (e.g., contrast), object-level (e.g., shape), and semantic-level attributes (e.g., faces) on 5,551 annotated objects. Compared with matched controls, people with ASD had a stronger image center bias regardless of object distribution, reduced saliency for faces and for locations indicated by social gaze, and yet a general increase in pixel-level saliency at the expense of semantic-level saliency. These results were further corroborated by direct analysis of fixation characteristics and investigation of feature interactions. Our results for the first time quantify atypical visual attention in ASD across multiple levels and categories of objects.