125 research outputs found

    Individual Differences and Biohybrid Societies

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    Contemporary robot design is influenced both by task domain (e.g., industrial manipulation versus social interaction) as well as by classification differences in humans (e.g., therapy patients versus museum visitors). As the breadth of robot use increases, we ask how will people respond to the ever increasing number of intelligent artefacts in their environment. Using the Paro robot as our case study we propose an analysis of individual differences in HRI to highlight the consequences individual characteristics have on robot performance. We discuss to what extent human-human interactions are a useful model of HRI

    Saying It with Light: A Pilot Study of Affective Communication Using the MIRO Robot

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    Recently, the concept of a ‘companion robot’ as a healthcare tool has been popularised, and even commercialised. We present MIRO, a robot that is biomimetic in aesthetics, morphology, behaviour, and control architecture. In this paper, we review how these design choices affect its suitability for a companionship role. In particular, we consider how emulation of the familiar body language and other emotional expressions of mammals may facilitate effective communication with na¨ıve users through the reliable evocation of intended perceptions of emotional state and intent. We go on to present a brief pilot study addressing the question of whether shared cultural signals can be relied upon, similarly, as components of communication systems for companion robots. Such studies form part of our ongoing effort to understand and quantify human responses to robot expressive behaviour and, thereby, develop a methodology for optimising the design of social robots by accounting for individual and cultural differences

    MIRO: A Versatile Biomimetic Edutainment Robot

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    Here we present MIRO, a companion robot designed to engage users in science and robotics via edutainment. MIRO is a robot that is biomimetic in aesthetics, morphology, behaviour, and control architecture. In this paper, we review how these design choices affect its suitability for a companionship role. In particular, we consider how MIRO's emulation of familiar mammalian body language as one component of a broader biomimetic expressive system provides effective communication of emotional state and intent. We go on to discuss how these features contribute to MIRO's potential in other domains such as healthcare, education, and research

    Optimising robot personalities for symbiotic interaction

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    The Expressive Agents for Symbiotic Education and Learning (EASEL) project will explore human-robot symbiotic interaction (HRSI) with the aim of developing an understanding of symbiosis over long term tutoring interactions. The EASEL system will be built upon an established and neurobiologically grounded architecture - Distributed Adaptive Control (DAC). Here we present the design of an initial experiment in which our facially expressive humanoid robot will interact with children at a public exhibition. We discuss the range of measurements we will employ to explore the effects our robot's expressive ability has on interaction with children during HRSI, with the aim of contributing optimal robot personality parameters to the final EASEL model. © 2014 Springer International Publishing

    Antimicrobials from sea food [abstract]

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    Designing robot personalities for human-robot symbiotic interaction in an educational context

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    The Expressive Agents for Symbiotic Education and Learning project explores human-robot symbiotic interaction with the aim to understand the development of symbiosis over long-term tutoring interactions. The final EASEL system will be built upon the neurobiologically grounded architecture - Distributed Adaptive Control. In this paper, we present the design of an interaction scenario to support development of the DAC, in the context of a synthetic tutoring assistant. Our humanoid robot, capable of life-like simulated facial expressions, will interact with children in a public setting to teach them about exercise and energy. We discuss the range of measurements used to explore children’s responses during, and experiences of, interaction with a social, expressive robot

    Automatic recognition of child speech for robotic applications in noisy environments

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    Automatic speech recognition (ASR) allows a natural and intuitive interface for robotic educational applications for children. However there are a number of challenges to overcome to allow such an interface to operate robustly in realistic settings, including the intrinsic difficulties of recognising child speech and high levels of background noise often present in classrooms. As part of the EU EASEL project we have provided several contributions to address these challenges, implementing our own ASR module for use in robotics applications. We used the latest deep neural network algorithms which provide a leap in performance over the traditional GMM approach, and apply data augmentation methods to improve robustness to noise and speaker variation. We provide a close integration between the ASR module and the rest of the dialogue system, allowing the ASR to receive in real-time the language models relevant to the current section of the dialogue, greatly improving the accuracy. We integrated our ASR module into an interactive, multimodal system using a small humanoid robot to help children learn about exercise and energy. The system was installed at a public museum event as part of a research study where 320 children (aged 3 to 14) interacted with the robot, with our ASR achieving 90% accuracy for fluent and near-fluent speech

    The effects of robot facial emotional expressions and gender on child-robot interaction in a field study

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    Emotions, and emotional expression, have a broad influence on social interactions and are thus a key factor to consider in developing social robots. This study examined the impact of life-like affective facial expressions, in the humanoid robot Zeno, on children’s behaviour and attitudes towards the robot. Results indicate that robot expressions have mixed effects depending on participant gender. Male participants interacting with a responsive facially expressive robot showed a positive affective response and indicated greater liking towards the robot, compared to those interacting with the same robot maintaining a neutral expression. Female participants showed no marked difference across the conditions. We discuss the broader implications of these findings in terms of gender differences in human–robot interaction, noting the importance of the gender appearance in robots (in this case, male) and in relation to advancing the understanding of how interactions with expressive robots could lead to task-appropriate symbiotic relationships

    Comprehensive clinical and molecular analysis of 12 families with type 1 recessive cutis laxa.

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    Autosomal recessive cutis laxa type I (ARCL type I) is characterized by generalized cutis laxa with pulmonary emphysema and/or vascular complications. Rarely, mutations can be identified in FBLN4 or FBLN5. Recently, LTBP4 mutations have been implicated in a similar phenotype. Studying FBLN4, FBLN5, and LTBP4 in 12 families with ARCL type I, we found bi-allelic FBLN5 mutations in two probands, whereas nine probands harbored biallelic mutations in LTBP4. FBLN5 and LTBP4 mutations cause a very similar phenotype associated with severe pulmonary emphysema, in the absence of vascular tortuosity or aneurysms. Gastrointestinal and genitourinary tract involvement seems to be more severe in patients with LTBP4 mutations. Functional studies showed that most premature termination mutations in LTBP4 result in severely reduced mRNA and protein levels. This correlated with increased transforming growth factor-beta (TGFβ) activity. However, one mutation, c.4127dupC, escaped nonsense-mediated decay. The corresponding mutant protein (p.Arg1377Alafs(*) 27) showed reduced colocalization with fibronectin, leading to an abnormal morphology of microfibrils in fibroblast cultures, while retaining normal TGFβ activity. We conclude that LTBP4 mutations cause disease through both loss of function and gain of function mechanisms
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