{"id":null,"code":"DTEK2103","name":{"valueFi":"Robot Learning","valueEn":"Robot Learning","valueSv":"Robot Learning"},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[],"createdAt":1790534319516,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"On completion of the course, the student will be able to:\r\n* Understand and apply the theoretical foundations of machine learning for robotics, including key concepts in probability theory, statistics, and dynamical systems modeling in robot learning.\r\n* Design and implement data-driven methods for robotic perception and decision-making across diverse robotic platforms, including robotic manipulators and mobile rovers.\r\n* Apply data collection and processing methodologies for robot learning and utilize simulation environments for training and evaluating learning-based robotic systems.\r\n* Formulate robotic decision-making problems as Markov Decision Processes (MDPs) and implement fundamental reinforcement learning algorithms, including Q-learning and deep reinforcement learning methods.\r\n* Develop and evaluate learning-based models in robotics, including system dynamics learning, perception model learning, and feature selection, using practical robot learning frameworks and simulation-based experiments.\r\n* Demonstrate familiarity with emerging paradigms in robot learning, including imitation learning, Vision-Language-Action (VLA) models, and other recent advances in intelligent robotic systems.\r\n* Use Python-based tools to implement, test, and evaluate machine learning models for robotic applications.","valueEn":"On completion of the course, the student will be able to:\r\n* Understand and apply the theoretical foundations of machine learning for robotics, including key concepts in probability theory, statistics, and dynamical systems modeling in robot learning.\r\n* Design and implement data-driven methods for robotic perception and decision-making across diverse robotic platforms, including robotic manipulators and mobile rovers.\r\n* Apply data collection and processing methodologies for robot learning and utilize simulation environments for training and evaluating learning-based robotic systems.\r\n* Formulate robotic decision-making problems as Markov Decision Processes (MDPs) and implement fundamental reinforcement learning algorithms, including Q-learning and deep reinforcement learning methods.\r\n* Develop and evaluate learning-based models in robotics, including system dynamics learning, perception model learning, and feature selection, using practical robot learning frameworks and simulation-based experiments.\r\n* Demonstrate familiarity with emerging paradigms in robot learning, including imitation learning, Vision-Language-Action (VLA) models, and other recent advances in intelligent robotic systems.\r\n* Use Python-based tools to implement, test, and evaluate machine learning models for robotic applications.","valueSv":"On completion of the course, the student will be able to:\r\n* Understand and apply the theoretical foundations of machine learning for robotics, including key concepts in probability theory, statistics, and dynamical systems modeling in robot learning.\r\n* Design and implement data-driven methods for robotic perception and decision-making across diverse robotic platforms, including robotic manipulators and mobile rovers.\r\n* Apply data collection and processing methodologies for robot learning and utilize simulation environments for training and evaluating learning-based robotic systems.\r\n* Formulate robotic decision-making problems as Markov Decision Processes (MDPs) and implement fundamental reinforcement learning algorithms, including Q-learning and deep reinforcement learning methods.\r\n* Develop and evaluate learning-based models in robotics, including system dynamics learning, perception model learning, and feature selection, using practical robot learning frameworks and simulation-based experiments.\r\n* Demonstrate familiarity with emerging paradigms in robot learning, including imitation learning, Vision-Language-Action (VLA) models, and other recent advances in intelligent robotic systems.\r\n* Use Python-based tools to implement, test, and evaluate machine learning models for robotic applications."}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"* **Mathematical and Control Foundations**: Review of probability theory, discrete-time control systems, and fundamental mathematical tools for robot learning.\r\n* **Reinforcement Learning Methods**: Including (1) model-based approaches leveraging Model Predictive Control (MPC), and (2) model-free approaches (Q-learning and deep reinforcement learning).\r\n* **Theoretical Frameworks**: Markov Decision Processes (MDPs) and core concepts of probabilistic inference, supported by practical examples.\r\n* **Learning Models in Robotics**: System dynamics learning (neural network-based dynamics models), perception model learning (self-supervised learning methods implemented with convolutional neural networks and transformer-based architectures), and feature selection methods (principal component analysis and attention mechanisms) in robotic systems.\r\n* The course concludes with an introduction to emerging directions in robot learning, including imitation learning, Vision-Language-Action (VLA) models, and other recent advances shaping the future of intelligent robotic systems.\r\n* **Training and Data**: Training methods include both offline learning from datasets and interactive/online learning approaches. Data will be collected from a variety of robotic platforms, including stationary robotic systems and mobile robots.\r\n* **Course Structure and Assessment**: The course includes project work, programming assignments, and a final examination. However, the primary emphasis of assessment is placed on project reports and their practical implementation.","valueEn":"* **Mathematical and Control Foundations**: Review of probability theory, discrete-time control systems, and fundamental mathematical tools for robot learning.\r\n* **Reinforcement Learning Methods**: Including (1) model-based approaches leveraging Model Predictive Control (MPC), and (2) model-free approaches (Q-learning and deep reinforcement learning).\r\n* **Theoretical Frameworks**: Markov Decision Processes (MDPs) and core concepts of probabilistic inference, supported by practical examples.\r\n* **Learning Models in Robotics**: System dynamics learning (neural network-based dynamics models), perception model learning (self-supervised learning methods implemented with convolutional neural networks and transformer-based architectures), and feature selection methods (principal component analysis and attention mechanisms) in robotic systems.\r\n* The course concludes with an introduction to emerging directions in robot learning, including imitation learning, Vision-Language-Action (VLA) models, and other recent advances shaping the future of intelligent robotic systems.\r\n* **Training and Data**: Training methods include both offline learning from datasets and interactive/online learning approaches. Data will be collected from a variety of robotic platforms, including stationary robotic systems and mobile robots.\r\n* **Course Structure and Assessment**: The course includes project work, programming assignments, and a final examination. However, the primary emphasis of assessment is placed on project reports and their practical implementation.","valueSv":"* **Mathematical and Control Foundations**: Review of probability theory, discrete-time control systems, and fundamental mathematical tools for robot learning.\r\n* **Reinforcement Learning Methods**: Including (1) model-based approaches leveraging Model Predictive Control (MPC), and (2) model-free approaches (Q-learning and deep reinforcement learning).\r\n* **Theoretical Frameworks**: Markov Decision Processes (MDPs) and core concepts of probabilistic inference, supported by practical examples.\r\n* **Learning Models in Robotics**: System dynamics learning (neural network-based dynamics models), perception model learning (self-supervised learning methods implemented with convolutional neural networks and transformer-based architectures), and feature selection methods (principal component analysis and attention mechanisms) in robotic systems.\r\n* The course concludes with an introduction to emerging directions in robot learning, including imitation learning, Vision-Language-Action (VLA) models, and other recent advances shaping the future of intelligent robotic systems.\r\n* **Training and Data**: Training methods include both offline learning from datasets and interactive/online learning approaches. Data will be collected from a variety of robotic platforms, including stationary robotic systems and mobile robots.\r\n* **Course Structure and Assessment**: The course includes project work, programming assignments, and a final examination. However, the primary emphasis of assessment is placed on project reports and their practical implementation."}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"Lectures, exercises and/or team projects.","valueEn":"Lectures, exercises and/or team projects.","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"The course is implemented through a combination of lectures, supervised laboratory sessions, programming assignments, and project-based learning.","valueEn":"The course is implemented through a combination of lectures, supervised laboratory sessions, programming assignments, and project-based learning.","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"**This course will be available starting from the academic year 2027-2028.**","valueEn":"**This course will be available starting from the academic year 2027-2028.**","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"0-5","valueEn":"0-5","valueSv":"0-5"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"The course includes project work, programming assignments, and a final examination. However, the primary emphasis of assessment is placed on project reports and their practical implementation.","valueEn":"The course includes project work, programming assignments, and a final examination. 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