Robot Learning for Planning and Control

3 credits · College of Engineering catalogue

Advisory Prerequisite: None. Enforced Prerequisite: Junior or senior standing, and Linear Algebra (ROB 101 or MATH 214 or MATH 217) and EECS 281; No OP/F. Minimum grade requirement of “C” for enforced prerequisite. Credit Exclusions: Only one course may earn credit from ROB 429 and ROB 529. (3 credits) Introduction to modern machine learning methods for control and planning in robotics. Topics include function approximation, learning dynamics, using learned dynamics in control and planning, handling uncertainty in learned models, learning from demonstration, and reinforcement learning. Students implement the above learning algorithms on robots in simulation.