CS 6756

CS 6756

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.

View Enrollment Information

Syllabi: none
  •   Regular Academic Session. 

  • 3 Credits Graded

  • 18381 CS 6756   LEC 001

    • TR Thurston Hall 203
    • Aug 22 - Dec 5, 2022
    • Choudhury, S

  • 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 cs-course-enroll@cornell.edu. 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