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IN2107 Variational Inference in Robotics

Course 0000001967 in SS 2023

Assignment to Modules

This course is not assigned to any module.

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks The seminar focuses on variational inference methods that are applicable to robotic perception and control problems. In recent years, applying machine learning to sequential data of systems has developed as a promising path to the application of control methods to very complex systems, such as robotic systems perceived through noisy sensors. One promising direction is that of variational inference, where high-dimensional and hence intractable problems of Bayesian inference can be approximated through stochastic optimization techniques. Here, the member of a family of distributions closest to the true posterior distribution is determined. This approximate distribution can then be used in place of the true one, yielding solutions to learning, inference or control problems such as system identification, state estimation, manipulation, localization or navigation. In this course, students will familiarize themselves with the methodology of variational inference in a deep learning framework. For that, fundamental knowledge such as machine learning, control, and robotics will be refreshed. Typical problems of robotics will be phrased as probabilistic inference and solutions based on recent developments will be studied and discussed. Examples are Dreamer, Learning To Fly and variational inference-based SLAM: - - -
Links E-Learning course (e. g. Moodle)
TUMonline entry
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