In this work, we model multiple natural language learning in a developmental neuroscience-inspired architecture. The ANNABELL model (Artificial Neural Network with Adaptive Behaviour Exploited for Language Learning), is a large-scale neural network, however, unlike most deep learning methods that solve natural language processing (NLP) tasks, it does not represent an empirical engineering solution for specific NLP problems; rather, its organisation complies with findings from cognitive neuroscience, particularly the multi-compartment working memory models. The system is appropriately trained to understand the level of cognitive development required for language acquisition and the robustness achieved in learning simultaneously four languages, using a corpus of text-based exchanges of developmental complexity. The selected languages, Greek, Italian and Albanian, besides English, differ significantly in structure and complexity. Initially, the system was validated in each language alone and was then compared with the open-ended cumulative training, in which languages are learned jointly, prior to querying with random language at random order. We aimed to assess if the model could learn the languages together to the same degree of skill as learning each apart. Moreover, we explored the generalisation skill in multilingual context questions and the ability to elaborate a short text of preschool literature. We verified if the system could follow a dialogue coherently and cohesively, keeping track of its previous answers and recalling them in subsequent queries. The results show that the architecture developed broad language processing functionalities, with satisfactory performances in each language trained singularly, maintaining high accuracies when they are acquired cumulatively.

The efforts to promote ageing-in-place of healthy older adults via cybernetic support are fundamental to avoid possible consequences associated with relocation to facilities, including the loss of social ties and autonomy, and feelings of loneliness. This requires an understanding of key factors that affect the involvement of robots in eldercare and the elderly willingness to embrace the robots' domestic use. Trust is argued to be the main foundation of an effective adult-care provider, which might be more significant if such providers are robots. Establishing, and maintaining trust usually involves two main dimensions: 1) the robot's reliability (i.e., performance) and 2) the robot's intrinsic attributes, including its degree of anthropomorphism and benevolence. We conducted a pilot study using a mixed methods approach to explore the extent to which these dimensions and their interaction influenced elderly trust in a humanoid social robot. Using two independent variables, type of attitude (warm, cold) and type of conduct (error, no-error), we aimed to investigate if the older adult participants would trust a purposefully faulty robot when the robot exerted a warm behaviour enhanced with non-functional touch more than a robot that did not, and in what way the robot error affected trust. Lastly, we also investigated the relationship between trust and a proxy variable of actual use of robots (i.e., intention to use robots at home). Given the volatile and context-dependent nature of trust, our close-to real-world scenario of elder-robot interaction involved the administration of health supplements, in which the severity of robot error might have a greater implication on the perceived trust.INDEX TERMS intention to use robots, anthropomorphism, eldercare, humanoid robot, human-robot interaction (HRI), perceived trust, robot attributes, robot care companion, robot performance, social robot.

Endowing robots with the ability to view the world the way humans do, to understand natural language and to learn novel semantic meanings when they are deployed in the physical world, is a compelling problem. Another significant aspect is linking language to action, in particular, utterances involving abstract words, in artificial agents. In this work, we propose a novel methodology, using a brain-inspired architecture, to model an appropriate mapping of language with the percept and internal motor representation in humanoid robots. This research presents the first robotic instantiation of a complex architecture based on the Baddeley's Working Memory (WM) model. Our proposed method grants a scalable knowledge representation of verbal and non-verbal signals in the cognitive architecture, which supports incremental open-ended learning. Human spoken utterances about the workspace and the task are combined with the internal knowledge map of the robot to achieve task accomplishment goals. We train the robot to understand instructions involving higher-order (abstract) linguistic concepts of developmental complexity, which cannot be directly hooked in the physical world and are not pre-defined in the robot's static self-representation. Our proposed interactive learning method grants flexible run-time acquisition of novel linguistic forms and real-world information, without training the cognitive model anew. Hence, the robot can adapt to new workspaces that include novel objects and task outcomes. We assess the potential of the proposed methodology in verification experiments with a humanoid robot. The obtained results suggest robust capabilities of the model to link language bi-directionally with the physical environment and solve a variety of manipulation tasks, starting with limited knowledge and gradually learning from the run-time interaction with the tutor, past the pre-trained stage.

Endowing robots with the role of social assistance in silver care could be a powerful tool to combat chronic loneliness in ageing adults. These robots can be tasked with functional and affective care to support quotidian living and grant companionship that helps lessen the burden of cognitive decline and impairment emerging from social isolation. To accomplish such imperative tasks, artificial agents must be adept at communicating naturally with the human elder. In this work, we aim to enable human-robot interaction by designing human-like verbal and nonverbal behaviours of an autonomous robot companion. We employed the robot on a trial run using customisable algorithms to address a range of needs, while thriving social and emotional attachment with the potential senior user, with the final intent being that such endeavours can help achieve quality ageing in place.

Artificial intelligence and robotic solutions are seeing rapid development for use across multiple occupations and sectors, including health and social care. As robots grow more prominent in our work and home environments, whether people would favour them in receiving useful advice becomes a pressing question. In the context of human–robot interaction (HRI), little is known about people’s advice-taking behaviour and trust in the advice of robots. To this aim, we conducted an experimental study with older adults to measure their trust and compliance with robot-based advice in health-related situations. In our experiment, older adults were instructed by a fictional human dispenser to ask a humanoid robot for advice on certain vitamins and over-the-counter supplements supplied by the dispenser. In the first experimented condition, the robot would give only information-type advice, i.e., neutral informative advice on the supplements given by the human. In the second condition, the robot would give recommendation-type advice, i.e., advice in favour of more supplements than those suggested initially by the human. We measured the trust of the participants in the type of robot-based advice, anticipating that they would be more trusting of information-type advice. Moreover, we measured the compliance with the advice, for participants who received robot-based recommendations, and a closer proxy of the actual use of robot health advisers in home environments or facilities in the foreseeable future. Our findings indicated that older adults continued to trust the robot regardless of the type of advice received, highlighting a type of protective role of robot-based recommendations on their trust. We also found that higher trust in the robot resulted in higher compliance with its advice. The results underpinned the likeliness of older adults welcoming a robot at their homes or health facilities.