Designing for Autonomy, Competence and Relatedness in Robot-Assisted Language Learning

The current number of immigrants has risen quickly in recent years due to globalization. People move to another country for economic, educational, emotional, and other reasons. As a result, immigrants need to learn the host language to integrate into their new living environment. However, the process of learning the host language for adult immigrants faces many challenges. Among those challenges, maintaining intrinsic motivation is critical for a long-term language study process and the well-being of adult immigrants. Self-Determination Theory (SDT) is a popular theoretical framework that explains human motivation, especially intrinsic motivation, through a psychological approach to understand its nature. According to SDT, humans are intrinsically motivated through the satisfaction of the three basic needs of Autonomy, Competence, and Relatedness. Many researchers have applied the theory to different topics and directions, including language learning. On the other hand, social robots have been used extensively in the language learning context due to their physical embodiments and the application of artificial intelligence in robotics. Furthermore, research has proven that social robots can create a relaxed and engaging learning environment, thus motivating language learners. The thesis designs and implements a RALL application called SAMQ using QTrobot, a humanoid social robot capable of producing body gestures, displaying different facial expressions, and multilingual communication. The study aims to investigate SAMQ’s ability to evoke intrinsic motivations of adult immigrants in learning the Finnish language. While previous research focuses on English as the second language (L2) and targets children, this thesis’s L2 is Finnish, and the learners are adult immigrants. The thesis conducts semi-structured interviews during the Pre-study phase (N=6) to gather real insights from adult immigrants living in Finland, to understand demotivating factors in their language learning experience and the unsatisfied aspects of the three basic needs. The qualitative findings from the Pre-study contribute to the design and implementation of two versions of SAMQ, aiming at evoking intrinsic motivations through satisfying unmet needs. The first version is a Quiz-only program that tests several assumptions regarding human-robot interaction (HRI). The final version of SAMQ is a more comprehensive language learning application that supports two modes of study: Learning and Quizzes. It consists of multiple modifications that address all adult immigrants’ basic needs while additionally promoting intrinsic motivation through media. The final Evaluation of SAMQ (N=6) includes a questionnaire and a semi-structured interview. The quantitative results of the questionnaire validated the ability of using social robots to evoke adult learners’ intrinsic motivation in the RALL context. The qualitative findings from the research high-light the importance of social robots’ physical embodiments in eliciting intrinsic motivation for adult learners through satisfying Relatedness. In addition, the use of voice modality creates a genuine HRI for adult learners, fulfilling both Autonomy and Competence, resulting in an engaging and smooth learning experience. Besides that, the use of adult learners’ L1 plays a crucial role in facilitating a relaxed and familiar learning environment, supplying both Competence and Relatedness. Moreover, multimedia learning materials make the learning experience more vivid and attractive. Ultimately, the result shows that accessibility and flexibility are essential attributes for adult learners to maintain their motivation for long-term language study through the satisfaction of Autonomy. Finally, the thesis proposes a design guideline for the RALL context. It consists of five design implications for evoking intrinsic motivation in adult learners through satisfying the three basic psychological needs of Autonomy, Competence, and Relatedness. The design guideline acts as a proposal for future design and implementation of RALL programs for adults and contributes to developing the human-robot interaction field

Are There Brain-Based Predictors of the Ability to Learn a New Skill in Healthy Ageing and Can They Help in the Design of Effective Therapy after Stroke?

This thesis aimed at looking for neural correlates of motor adaptation as a model of rehabilitation after brain injury. Healthy adults across the lifespan and stroke patients were tested in a force-field learning paradigm. This thesis focuses on EEG analysis and the complex relationship of brain-derived measures with observed behaviour. To describe each domain in detail, the focus was first on finding group differences between older and younger healthy adults in a similar manner as it was later between stroke patients versus healthy controls. The analyses were finalised by looking for relationships between the EEG and motor performance data in a multiple linear regression approach. As candidate EEG biomarkers of motor adaptation, error related event related potential around movement onset in the frontocentral electrodes was chosen in time domain. In the time-frequency domain, the focus was on movement related beta band spectral perturbation, looking at the electrodes over the primary motor cortex and the frontocentral ROI found significant in the time domain. Finally, functional connectivity was analysed focusing first on electrode over the primary motor cortex contralateral to the movement as a seed region, to narrow down the analysis to bilateral motor cortex connectivity and connectivity between primary motor cortex contralateral to the movement and the frontocentral region identified as important in the time domain analysis. The crucial part of the project was analysing the relationship between the neural and kinematic measures. The most important predictor of summed error in motor adaptation was the connectivity between C3 and C4 electrode at the baseline prestimulus period in motor adaptation condition and pinch asymmetry. Higher prestimulus interhemispheric connectivity was associated with bigger deviation from the optimal trajectory. When looking at summed error dynamic derivative as a dependent variable - performance index - it was the ERP at the central error-related ROI that explained the most variance. It can be concluded that higher baseline interhemispheric connectivity can be a reflection of a maladaptive process, perhaps related to increased interhemispheric inhibition. It is important to also note that the same connectivity at different timepoints in the movement can be of different significance - differences between stroke patients and controls were present in the postmovement period. In conclusion, brain information could be helpful for e.g. stratifying patients into different intensity programs based on their predicted potential to recover. Moreover, brain information could be utilised to apply closed-loop systems modulating the intensity of tasks to reach the optimal brain state that facilitates learning. I believe this work will help incorporating brain-derived measures in informing neurorehabilitation programmes in the future

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Segmentation of surgical tools from laparoscopy images

Relatório de projeto de mestrado em Engenharia BiomédicaCirurgias roboticamente assistidas têm vindo a substituir as cirurgias abertas com enorme impacto no tempo de convalescença do paciente e consequentemente em tudo o que isso implica, economia de recursos no sector da saúde e a retoma antecipada das atividades laborais do paciente. Este tipo de cirurgia auxiliada por um sistema robótico é guiado por uma câmara laparoscópica, facultando ao médico uma visão das partes anatómicas do paciente. A fim do cirurgião se encontrar apto para operar este equipamento tem de passar por inúmeras horas de formação, tornando o processo desgastante e dispendioso. Para além do referido, a manipulação dos instrumentos cirúrgicos em concordância com a câmara laparoscópica não é de todo um processo intuitivo, ou seja, os erros de natureza subjetiva não são erradicados. A diretiva desta tese é o desenvolvimento de um sistema automático capaz de segmentar instrumentos cirúrgicos, possibilitando desta forma a monitorização constante da posição dos instrumentos. Para tal foram explorados diferentes modelos de aprendizagem automática. Numa segunda fase, foram considerados métodos que pudessem ser incorporados no modelo base. Tendo-se encontrado uma resposta, partiu-se para a comparação dos modelos previamente selecionados, com o modelo base e ainda com o otimizado. Numa terceira abordagem, de forma a melhorar as métricas que serviram de comparação, procurou-se por soluções alternativas, nomeadamente a geração de dados artificiais. Neste ponto, deparou-se com duas possibilidades, uma baseada em sistemas de aprendizagem autónoma por competição e outra em sistemas de aprendizagem de síntese de imagens a partir de ruido com densidade espectral sucessivamente incrementada. Ambas as abordagens permitiram o aumento da base de dados tendo-se aferido a sua eficácia por comparação do efeito do aumento de dados nos sistemas de segmentação. O sistema proposto pode vir a ser implementado em cirurgias roboticamente assistidas, necessitando apenas de mínimas alterações.Robotic-assisted surgeries have been replacing open surgeries with a significant impact on patient recovery time, and consequently, on various aspects such as healthcare resource savings and the early resumption of the patient's work activities. This type of surgery, assisted by a robotic system, is guided by a laparoscopic camera, providing the surgeon with a view of the patient's anatomical structures. To operate this equipment, surgeons must undergo numerous hours of training, making the process exhaustive and costly. In addition, manipulating surgical instruments in coordination with the laparoscopic camera is not an intuitive process, meaning errors of a subjective nature are not eliminated. The objective of this thesis is the development of an automated system capable of segmenting surgical instruments, thereby enabling constant monitoring of their positions. Various machine learning models were explored to address this issue. In a second phase, methods that could be incorporated into the base model were considered. Once a solution was found, a comparison was made between the previously selected models, the base model, and the optimized model. In a third approach, with the aim of improving the comparison metrics, alternative solutions were sought, including the generation of synthetic data. At this point, two possibilities were encountered, one based on autonomous learning systems through competition and the other on image synthesis learning systems from progressively increasing noise spectral density. Both approaches expanded the available database, and their effectiveness was evaluated by comparing the impact of data augmentation on segmentation systems. The proposed system can potentially be implemented in robotic-assisted surgeries with minimal modifications

Annals of Scientific Society for Assembly, Handling and Industrial Robotics

This Open Access proceedings present a good overview of the current research landscape of industrial robots. The objective of MHI Colloquium is a successful networking at academic and management level. Thereby the colloquium is focussing on a high level academic exchange to distribute the obtained research results, determine synergetic effects and trends, connect the actors personally and in conclusion strengthen the research field as well as the MHI community. Additionally there is the possibility to become acquainted with the organizing institute. Primary audience are members of the scientific association for assembly, handling and industrial robots (WG MHI)

Intelligence artificielle: Les défis actuels et l'action d'Inria - Livre blanc Inria

Livre blanc Inria N°01International audienceInria white papers look at major current challenges in informatics and mathematics and show actions conducted by our project-teams to address these challenges. This document is the first produced by the Strategic Technology Monitoring & Prospective Studies Unit. Thanks to a reactive observation system, this unit plays a lead role in supporting Inria to develop its strategic and scientific orientations. It also enables the institute to anticipate the impact of digital sciences on all social and economic domains. It has been coordinated by Bertrand Braunschweig with contributions from 45 researchers from Inria and from our partners. Special thanks to Peter Sturm for his precise and complete review.Les livres blancs d’Inria examinent les grands défis actuels du numérique et présentent les actions menées par noséquipes-projets pour résoudre ces défis. Ce document est le premier produit par la cellule veille et prospective d’Inria. Cette unité, par l’attention qu’elle porte aux évolutions scientifiques et technologiques, doit jouer un rôle majeur dans la détermination des orientations stratégiques et scientifiques d’Inria. Elle doit également permettre à l’Institut d’anticiper l’impact des sciences du numérique dans tous les domaines sociaux et économiques. Ce livre blanc a été coordonné par Bertrand Braunschweig avec des contributions de 45 chercheurs d’Inria et de ses partenaires. Un grand merci à Peter Sturm pour sa relecture précise et complète. Merci également au service STIP du centre de Saclay – Île-de-France pour la correction finale de la version française

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities