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It develops three primitive-based solutions: generic control of bipedal walking with a minimal shared set of Motor Primitives represented by stable dynamical systems; robust detection of irregular human gait using compact time-invariant perceptual memories built from shared Perceptual Primitives; and identification of muscular fatigue during repetitive upper-limb motion via gradual, significant changes in Muscle Synergies evaluated with time- and frequency-domain indicators. Experimental validation supports lifelong learning-inspired cognitive strategies for autonomous machines, complementary cognitive architectures, and improved human behavior monitoring and management.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/combining-dynamical-systems-with-machine-learning-for-adaptive-control-of-legged-robots/290702/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/combining-dynamical-systems-with-machine-learning-for-adaptive-control-of-legged-robots/290702.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-08","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the thesis?","Question",{"text":112,"@type":113},"To investigate the role of primitive structures across different levels of natural and artificial motor generation and control for adaptive strategies.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is generic control of bipedal legged walking achieved?",{"text":117,"@type":113},"By using a minimal shared set of Motor Primitives across humanoid platforms, represented with stable dynamical systems.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the thesis detect irregular gait in humans?",{"text":121,"@type":113},"It builds generic perceptual memories of nominal gait, sharing Perceptual Primitives across input dimensions to obtain a compact, robust, time-invariant representation.",{"name":123,"@type":110,"acceptedAnswer":124},"How is muscular fatigue identified in repetitive upper-limb movements?",{"text":125,"@type":113},"By tracking gradual and significant changes in the recruitment of Muscle Synergies using multiple time- and frequency-domain indicators.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},290702,1790039037,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Combining Dynamical Systems with  \nUMinho | 2025 João André Machine Learning Adaptivefor Control of Legged Robots  \nUniversidade do Minho  \nEscola de Engenharia  \nJoão André  \nCombining Dynamical Systems with Machine Learning for Adaptive Control of Legged Robots  \nabril de 2025  \nUniversidade do Minho  \nEscola de Engenharia  \nJoão André  \nCombining Dynamical Systems with Machine Learning for Adaptive Control of Legged Robots  \nTese de Doutoramento  \nPrograma Doutoral em Engenharia Biomédica Escola de Engenharia  \nTrabalho efetuado sob a orientação da  \nProfessora Doutora Cristina Santos  \ne do  \nProfessor Doutor Luís Paulo Reis  \nDireitos de Autor e Condições de Utilização do Trabalho por Terceiros  \nEste é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada.  \nCaso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições nãoprevistas no licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidadedo Minho.  \nLicença concedida aos utilizadores deste trabalho  \nAtribuição  \nCC BY  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nAcknowledgements  \nThe work described in this thesis was funded by the Foundation for Science and Technology (FCT) through the Doctoral Grant SFRH/BD/107891/2015 .  \nStatement of Integrity  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration.  \nI further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho.  \nResumo  \nControlo Adaptativo de Robôs com Pernas através da combinação de Sistemas Dinâmicos  \ncom Machine Learning  \nNesta tese de doutoramento é investigado o papel de estruturas primitivas em differentes níveis de geração e controlo motor em sistems naturais e artificiais. Através de diferentes estudos experimentais, são propostas soluções práticas para diferentes problemas de engenharia, numa abordagem multidisciplinar que explora elementos de baixa dimensionalidade de natureza cinemática, perceptual e neuromuscular. Especificamente, o 1) controlo genérico de locomoção bípede artificial é conseguido empregando apenasuma quantidade mínima de Primitivas Motoras (MPs) partilhadas em dois robôs humanóides, usando sistemas dinâmicos na representação de MPs. a 2) deteção de marcha irregular e atípica em humanos é efetuada através da construção de memórias perceptuais genéricas representando locomoção bípedenormal, com differentes Primitivas Perceptuais (MPs) partilhadas por differentes dimensões, e levando auma representação compacta, robusta e invariante no tempo. e a 3) identificação de fadiga muscular em movimentos repetitivos dos membros superiores, com base em variações graduais e significantivas norecrutamento de Sinergias Musculares (MSs), quantificadas através de múltiplos indicadores no domínio temporal e espectral. A validação experimental dos modelos e algoritmos introduzidos nesta tese demonstra a relevância de estruturas primitivas na construção de estratégias cognitivas artificiais, em particular dentro do paradigma de Aprendizagem Contínua ou Lifelong Learning (L2) . Na prática, os métodos emodelos propostos são particularmente apelativos na i) construção de máquinas autónomas capazes delidar eficientemente com tarefas e ambientes complexos e com alta dimensionalidade, podem complementar, ii) podem complementar arquitecturas cognitivas e ferramentas de raciocínio artificial existentes, e iii) contribuir no desenho de soluções de monitorização e gestão de comportamento humano, possibilitando uma mais rica coexistência entre robôs e humanos.  \nP","cbCaijTQDjqfGE7s","https://ap.wps.com/l/cbCaijTQDjqfGE7s","pdf",9949268,292,"English","# Resumo\n## Controlo Adaptativo de Robôs com Pernas\n# Abstract\n## Role of primitive structures\n## Three primitive-based solutions\n## Experimental validation","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To investigate the role of primitive structures across different levels of natural and artificial motor generation and control for adaptive strategies.\"},{\"question\":\"How is generic control of bipedal legged walking achieved?\",\"answer\":\"By using a minimal shared set of Motor Primitives across humanoid platforms, represented with stable dynamical systems.\"},{\"question\":\"How does the thesis detect irregular gait in humans?\",\"answer\":\"It builds generic perceptual memories of nominal gait, sharing Perceptual Primitives across input dimensions to obtain a compact, robust, time-invariant representation.\"},{\"question\":\"How is muscular fatigue identified in repetitive upper-limb movements?\",\"answer\":\"By tracking gradual and significant changes in the recruitment of Muscle Synergies using multiple time- and frequency-domain indicators.\"}]","Combining Dynamical Systems with Machine Learning for Adaptive Control of Legged Robots | PDF",1789642394,736]