- Schedule of Classes - October 5, 2022 7:29PM EDT
- Course Catalog - October 5, 2022 7:14PM EDT
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.
Regular Academic Session.
Credits and Grading Basis
3 Credits Graded(Letter grades only)
Class Number & Section Details
- TR Thurston Hall 203
- Aug 22 - Dec 5, 2022
Instruction Mode: In Person
Enrollment is limited to CS PhD and CS MS students. For non CS PhD and MS students, please add yourself to the waitlist or send an email to email@example.com. See enrollment page for more details: https://www.cs.cornell.edu/courseinfo/enrollment/cs-6000-level-courses. Course Website: https://www.cs.cornell.edu/courses/cs6756/2022fa/#overview
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