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DTSTART;VALUE=DATE:20240501
DTEND;VALUE=DATE:20240601
DTSTAMP:20260415T053454
CREATED:20240424T081035Z
LAST-MODIFIED:20240424T104758Z
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SUMMARY:Data-Driven Robot Control
DESCRIPTION:Title: Data-Driven Robot Control\n The Maersk Mc Kinney Moller Institute\, SDU Robotics\n Teaching language: English\n Teachers: Christoffer Sloth chsl@mmmi.sdu.dk / Inigo Iturrate inju@mmmi.sdu.dk\n ECTS: 2.5 ECTS\n Period: May 2024\n Offered in: Odense \n Prerequisites\n It is recommended that students participating in the course have: \na.                         basic knowledge in control of robots \nb.                         basic knowledge in optimization \nc.                          basic knowledge in machine learning \n Content \nData-driven methods\, such as Gaussian processes\, make it possible to obtain models of unknown processes with uncertainty quantifications\, and have found widespread applications in recent years. This course gives an introduction to data-driven methods for robot control. \nThe course will start with a general introduction on the theory of Gaussian Process Regression [1]\, which will serve as a backbone for the remaining topics. \nThe theory will subsequently be exemplified through three use-cases: identification of inverse dynamics models of robotic manipulators [2]\, safety guarantees for uncertain dynamical systems [4]\, and learning of robot trajectories based on a given set of demonstrations [4]. \n[1] Wang\, Jie. “An intuitive tutorial to Gaussian processes regression.” Computing in Science & Engineering\, 2023. \n[2] J. S. de la Cruz\, W. Owen and D. Kulíc\, “Online learning of inverse dynamics via Gaussian Process Regression\,” 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems\, Vilamoura-Algarve\, Portugal\, 2012\, pp. 3583-3590\, doi: 10.1109/IROS.2012.6385817. \n[3] Y. Kim\, I. Iturrate\, J. Langaa and C. Sloth\, “Safe Robust Adaptive Control under Both Parametric and \nNon-Parametric Uncertainty”\, Advanced Robotics\, 2024. \n[2] M. Arduengo\, A. Colomé\, J. Lobo-Prat\, L. Sentis and Carme Torras\, “Gaussian-process-based robot learning from demonstration\,” J Ambient Intell Human Comput. 2023. https://doi-org.proxy1-bib.sdu.dk/10.1007/s12652-023-04551-7 \n Learning outcomes \nThe aim of the course is\, to give the student knowledge about: \n\nGaussian Process Regression and its application to robotics\n\nBe able to work with the following skills: \n\nQuantify the uncertainties of a dynamical system trajectories based on data\n\nAnd have the competences to: \n\n      Design controllers for uncertain dynamical systems using data-driven methods\n 	 \n\nTime of classes \nThe course will start in May 2024 and will have five sessions. \nThe course will last 5 days\, i.e.\, 40 hours. \n2.5 ECTS = 67.5 h (40 h teaching\, 15 h preparation\, 12.5 h hand-in)\n   \nMore information and registration:\n Via email to Christoffer Sloth (chsl@mmmi.sdu.dk) or Pia Mønster (pmkr@mmmi.sdu.dk).\n Deadline: 1 week before the classes start.\n   \nForm of instruction\n The teaching is a mixture of lectures and exercises\, where the students can apply the theory in practice.\n   \nExamination conditions\n Following is a prerequisite to attend the final project exam: \n\nParticipation in 80 % of the classes.\n\n Evaluation \nInternal examination with no co-examiner based on a submitted report with solutions to problems addressed during the classes. The assessment will be pass/fail.\n   \nPrice\n No charge \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/__trashed-9/
LOCATION:Campusvej 55\, Odense M
CATEGORIES:PhD Course
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