CMPSC 291I Introduction to Robot Learning

This graduate course gives an overview of machine learning for planning and control of complex dynamical systems. Topics include reinforcement learning in continuous state/action spaces, data-driven dynamics models, imitation learning, online policy optimization, and robustness/adaptivity to environment shifts. Students will develop a thorough understanding of fundamental algorithms and learn to select appropriate methods based on the problem's interaction and information protocols.

Students should have a strong foundation in linear algebra, calculus, and probability, and be comfortable implementing numerical algorithms. Background in machine learning, optimization, dynamical systems, and control will be helpful but not required.