CS 6756

Learning for Robot Decision Making

Course Description

Course information provided by the Courses of Study 2022-2023.

Advances in machine learning have fueled progress towards deploying real-world robots from assembly lines to self-driving. Learning to make better decisions for robots presents a unique set of challenges. Robots must be safe, learn online from interactions with the environment, and predict the intent of their human partners. This graduate-level course dives into the various paradigms for robot learning and decision making and heavily focuses on algorithms, practical considerations, and features a strong programming component.

When Offered Fall.

Permission Note Enrollment limited to: graduate students or permission of instructor.
Prerequisites/Corequisites Prerequisite: CS 4780 and demonstrated knowledge of linear algebra and probability.

• Understand the fundamental concepts of online learning, reinforcement learning, and imitation learning in the context of robot decision making.
• Formulate existing as well as new problems in robotics as instances of these learning frameworks.
• Analyze tradeoffs in performance, sample complexity, and runtimes of various robot learning algorithms.
• Implement state-of-the-art robot learning algorithms and demonstrate performance on open-source benchmarks.

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