Software architecture for YOLO, a creativity-stimulating robot

YOLO is a social robot designed and developed to stimulate creativity in children through storytelling activities. Children use it as a character in their stories. This article details the artificial intelligence software developed for YOLO. The implemented software schedules through several Creativity Behaviors to find the ones that stimulate creativity more effectively. YOLO can choose between convergent and divergent thinking techniques, two important processes of creative thought. These techniques were developed based on the psychological theories of creativity development and on research from creativity experts who work with children. Besides promoting creativity, this software allows the creation of Social Behaviors that enable the robot to behave as a believable character. We built 3 main social behavior parameters: Exuberant, Aloof, and Harmonious. These behaviors are meant to ease immersive play and the process of character creation. The 3 social behaviors were based on psychological theories of personality and developed using children's input during co-design studies. Overall, this work presents the design, development, and usage of social robots that might nurture intrinsic human abilities, such as the ability to be creative.info:eu-repo/semantics/publishedVersio

Boosting children's creativity through creative interactions with social robots

Creativity is an ability with psychological and developmental benefits. Creative levels are dynamic and oscillate throughout life, with a first major decline occurring at the age of 7 years old. However, creativity is an ability that can be nurtured if trained, with evidence suggesting an increase in this ability with the use of validated creativity training. Yet, creativity training for young children (aged between 6-9 years old) appears as scarce. Additionally, existing training interventions resemble test-like formats and lack of playful dynamics that could engage children in creative practices over time. This PhD project aimed at contributing to creativity stimulation in children by proposing to use social robots as intervention tools, thus adding playful and interactive dynamics to the training. Towards this goal, we conducted three studies in schools, summer camps, and museums for children, that contributed to the design, fabrication, and experimental testing of a robot whose purpose was to re-balance creative levels. Study 1 (n = 140) aimed at testing the effect of existing activities with robots in creativity and provided initial evidence of the positive potential of robots for creativity training. Study 2 (n = 134) aimed at including children as co-designers of the robot, ensuring the robot’s design meets children’s needs and requirements. Study 3 (n = 130) investigated the effectiveness of this robot as a tool for creativity training, showing the potential of robots as creativity intervention tools. In sum, this PhD showed that robots can have a positive effect on boosting the creativity of children. This places social robots as promising tools for psychological interventions.Criatividade é uma habilidade com benefícios no desenvolvimento saudável. Os níveis de criatividade são dinâmicos e oscilam durante a vida, sendo que o primeiro maior declínio acontece aos 7 anos de idade. No entanto, a criatividade é uma habilidade que pode ser nutrida se treinada e evidências sugerem um aumento desta habilidade com o uso de programas validados de criatividade. Ainda assim, os programas de criatividade para crianças pequenas (entre os 6-9 anos de idade) são escassos. Adicionalmente, estes programas adquirem o formato parecido ao de testes, faltando-lhes dinâmicas de brincadeira e interatividade que poderão motivar as crianças a envolverem-se em práticas criativas ao longo do tempo. O presente projeto de doutoramento procurou contribuir para a estimulação da criatividade em crianças propondo usar robôs sociais como ferramenta de intervenção, adicionando dinâmicas de brincadeira e interação ao treino. Assim, conduzimos três estudos em escolas, campos de férias, e museus para crianças que contribuíram para o desenho, fabricação, e teste experimental de um robô cujo objetivo é ser uma ferramenta que contribui para aumentar os níveis de criatividade. O Estudo 1 (n = 140) procurou testar o efeito de atividade já existentes com robôs na criatividade e mostrou o potencial positivo do uso de robôs para o treino criativo. O Estudo 2 (n = 134) incluiu crianças como co-designers do robô, assegurando que o desenho do robô correspondeu às necessidades das crianças. O Estudo 2 (n = 130) investigou a eficácia deste robô como ferramenta para a criatividade, demonstrando o seu potencial para o treino da criatividade. Em suma, o presente doutoramento mostrou que os robôs poderão ter um potencial criativo em atividades com crianças. Desta forma, os robôs sociais poderão ser ferramentas promissoras em intervenções na psicologia

Creativity encounters between children and robots

Creativity is an intrinsic human ability with multiple benefits across the lifespan. Despite its importance, societies not always are well equipped with contexts for creativity stimulation; as a consequence, a major decline in creative abilities occurs at the age of 7 years old. We investigated the effectiveness of using a robotic system named YOLO as an intervention tool to stimulate creativity in children. During the intervention, children used YOLO as a character for their stories and through the interaction with the robot, creative abilities were stimulated. Our study (n = 62) included 3 experimental conditions: i) YOLO displayed behaviors based on creativity techniques; ii) YOLO displayed behaviors based on creativity techniques plus social behaviors; iii) YOLO was turned off, not displaying any behaviors. We measured children’s creative abilities at pre- and post-testing and their creative process through behavior analysis. Results showed that the interaction with YOLO contributed to higher creativity levels in children, specifically contributing to the generation of more original ideas during story creation. This study shows the potential of using social robots as tools to empower intrinsic human abilities, such as the ability to be creative.info:eu-repo/semantics/publishedVersio

Children as robot designers

We present the design process of the robot YOLO aimed at stimulating creativity in children. This robot was developed under a human-centered design approach with participatory design practices during two years and involving 142 children as active contributors at all design stages. The main contribution of this work is the development of methods and tools for child-centered robot design. We adapted existing participatory design practices used with adults to ft children’s development stages.We followed the Double-Diamond Design Process Model and rested the design process of the robot on the following principles: Low foor and wide walls, creativity provocations, open-ended playfulness, and disappointment avoidance through abstraction. The fnal product is a social robot designed for and with children. Our results show that YOLO increases their creativity during play, demonstrating a successful robot design project.We identifed several guidelines that made the design process successful: the use of toys as tools, playgrounds as spaces, the emphasis of playfulness for child expression, and child policies as allies for design studies. The design process described empowers children’s in the design of robots.info:eu-repo/semantics/publishedVersio

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Image Filtering Techniques for Object Recognition in Autonomous Vehicles

The deployment of autonomous vehicles has the potential to significantly lessen the variety of current harmful externalities, (such as accidents, traffic congestion, security, and environmental degradation), making autonomous vehicles an emerging topic of research. In this paper, a literature review of autonomous vehicle development has been conducted with a notable finding that autonomous vehicles will inevitably become an indispensable future greener solution. Subsequently, 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, have been built and analyzed for 2-D object recognition in the navigation system. While testing on the BDD100K dataset, YOLOv5s and EfficientNet-B7 appear to be the two best models. Finally, this study has proposed Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques to optimize the performance and sustainability of those 2 models. The results demonstrate that these filters could increase the mean average precision by up to 11.81%, reduce detection time by up to 43.98%, and significantly reduce energy consumption by up to 50.69% when applied to YOLOv5s and EfficientNet-B7 models. Overall, all the experiment results are promising and could be extended to other domains for semantic understanding of the environment. Additionally, various filtering algorithms for multiple object detection and classification could be applied to other areas. Different recommendations and future work have been clearly defined in this study