16-702 Fundamentals of Robot Behavior Learning
6 units, Carnegie Mellon University
As a core mini, this course will provide students with the fundamental knowledge and technical skills to begin exploring the topic of robot skill learning and collaborate with others in this field. The lectures will cover common core methods in imitation learning and reinforcement learning, including behavioral cloning, Bayesian optimization, value function methods, and policy search. Lectures will provide students with a solid theoretical foundation on these topics and common algorithms used in each one. Students will learn how to formulate and set up new learning problems such that the algorithms can be applied to their own work. To support hands-on learning, the course includes a set of programming assignments in which the students will implement and explore different learning algorithms using a robot simulation. A final exam will ensure that students have grasped the core concepts of the course and can communicate key ideas in this area.
Prerequisites: none listed
Offered: Spring 2027, Fall 2025
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