Overcoming the Reality Gap: Imitation and Reinforcement Learning Algorithms for Bipedal Robotic Locomotion Problems

dc.contributor.advisorMojica Nava, Eduardo Alirio
dc.contributor.authorYanguas Rojas, David Reinerio
dc.contributor.cvlacYanguas Rojas, Davidspa
dc.contributor.googlescholarDavid Yanguas-Rojasspa
dc.contributor.orcid0000-0001-5874-721Xspa
dc.contributor.researchgateDavid R. Yanguas Rojasspa
dc.contributor.researchgroupPrograma de Investigacion sobre Adquisicion y Analisis de Señales Paas-Unspa
dc.date.accessioned2024-01-24T20:37:35Z
dc.date.available2024-01-24T20:37:35Z
dc.date.issued2023
dc.descriptionilustraciones, diagramas, fotografíasspa
dc.description.abstractEsta tesis presenta una estrategia de entrenamiento de robots que utiliza técnicas de aprendizaje artificial para optimizar el rendimiento de los robots en tareas complejas. Motivado por los impresionantes logros recientes en el aprendizaje automático, especialmente en juegos y escenarios virtuales, el proyecto tiene como objetivo explorar el potencial de estas técnicas para mejorar las capacidades de los robots más allá de la programación humana tradicional a pesar de las limitaciones impuestas por la brecha de la realidad. El caso de estudio seleccionado para esta investigación es la locomoción bípeda, ya que permite dilucidar los principales desafíos y ventajas de utilizar métodos de aprendizaje artificial para el aprendizaje de robots. La tesis identifica cuatro desafíos principales en este contexto: la variabilidad de los resultados obtenidos de los algoritmos de aprendizaje artificial, el alto costo y riesgo asociado con la realización de experimentos en robots reales, la brecha entre la simulación y el comportamiento del mundo real, y la necesidad de adaptar los patrones de movimiento humanos a los sistemas robóticos. La propuesta consiste en tres módulos principales para abordar estos desafíos: Enfoques de Control No Lineal, Aprendizaje por Imitación y Aprendizaje por Reforzamiento. El módulo de Enfoques de Control No Lineal establece una base al modelar robots y emplear técnicas de control bien establecidas. El módulo de Aprendizaje por Imitación utiliza la imitación para generar políticas iniciales basadas en datos de captura de movimiento de referencia o resultados preliminares de políticas para crear patrones de marcha similares a los humanos y factibles. El módulo de Aprendizaje por Refuerzos complementa el proceso mejorando de manera iterativa las políticas paramétricas, principalmente a través de la simulación pero con el rendimiento en el mundo real como objetivo final. Esta tesis enfatiza la modularidad del enfoque, permitiendo la implementación de los módulos individuales por separado o su combinación para determinar la estrategia más efectiva para diferentes escenarios de entrenamiento de robots. Al utilizar una combinación de técnicas de control establecidas, aprendizaje por imitación y aprendizaje por refuerzos, la estrategia de entrenamiento propuesta busca desbloquear el potencial para que los robots alcancen un rendimiento optimizado en tareas complejas, contribuyendo al avance de la inteligencia artificial en la robótica no solo en sistemas virtuales sino en sistemas reales.spa
dc.description.abstractThe thesis introduces a comprehensive robot training framework that utilizes artificial learning techniques to optimize robot performance in complex tasks. Motivated by recent impressive achievements in machine learning, particularly in games and virtual scenarios, the project aims to explore the potential of these techniques for improving robot capabilities beyond traditional human programming. The case study selected for this investigation is bipedal locomotion, as it allows for elucidating key challenges and advantages of using artificial learning methods for robot learning. The thesis identifies four primary challenges in this context: the variability of results obtained from artificial learning algorithms, the high cost and risk associated with conducting experiments on real robots, the reality gap between simulation and real-world behavior, and the need to adapt human motion patterns to robotic systems. The proposed approach consists of three main modules to address these challenges: Non-linear Control Approaches, Imitation Learning, and Reinforcement Learning. The Non-linear Control module establishes a foundation by modeling robots and employing well-established control techniques. The Imitation Learning module utilizes imitation to generate initial policies based on reference motion capture data or preliminary policy results to create feasible human-like gait patterns. The Reinforcement Learning module complements the process by iteratively improving parametric policies, primarily through simulation but ultimately with real-world performance as the ultimate goal. The thesis emphasizes the modularity of the approach, allowing for the implementation of individual modules separately or their combination to determine the most effective strategy for different robot training scenarios. By employing a combination of established control techniques, imitation learning, and reinforcement learning, the framework seeks to unlock the potential for robots to achieve optimized performances in complex tasks, contributing to the advancement of artificial intelligence in robotics.eng
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en ingeniería mecánica y mecatrónicaspa
dc.format.extentxxi, 158 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/85427
dc.language.isoengspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería Mecánica y Mecatrónicaspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.armarcAutomation
dc.subject.ddc620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaspa
dc.subject.lembRobótica
dc.subject.lembRobotics
dc.subject.lembAutomatización
dc.subject.lembAlgoritmos
dc.subject.lembAlgorithms
dc.subject.proposalReinforcement learningeng
dc.subject.proposalHumanoid Roboticseng
dc.subject.proposalImitation Learningeng
dc.subject.proposalNon-Linear Controleng
dc.subject.proposalRobot Trainingeng
dc.subject.proposalBipedal Locomotioneng
dc.subject.proposalHumanoid Locomotioneng
dc.subject.proposalArtificial Learning Techniqueseng
dc.subject.proposalReality Gapeng
dc.subject.proposalSim to Realeng
dc.titleOvercoming the Reality Gap: Imitation and Reinforcement Learning Algorithms for Bipedal Robotic Locomotion Problemseng
dc.title.translatedSuperando la brecha de la realidad: Algoritmos de aprendizaje por imitación y por refuerzos para problemas de locomoción robótica bípedaspa
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentAudiovisualspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TDspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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