Computational Sensorimotor Learning
6.8200, Spring 2023
The course will provide an in-depth view of the state-of-the-art learning methods for control and the know-how to apply these techniques. The first half of the course will focus on hands-on experience through exercises. The second half will focus on current research directions and open questions. Topics will span reinforcement learning, self-supervised learning, imitation learning, model-based learning, and advanced deep learning architectures. By the end of the course, we hope you will be able to answer if learning-based control can help solve the problem of your interest, how to formulate the problem in the learning framework and what algorithms to use. It will also prepare you for research in this area.