CS 4756

Robot Learning

Course Description

Course information provided by the Courses of Study 2024-2025.

How do we get robots out of the labs and into the real world with all it's complexities? Robots must solve two fundamental problems -- (1) Perception: Sense the world using different modalities and (2) Decision making: Act in the world by reasoning over decisions and their consequences. Machine learning promises to solve both problems in a scalable way using data. However, it has fallen short when it comes to robotics. This course dives deep into robot learning, looks at fundamental algorithms and challenges, and case-studies of real-world applications from self-driving to manipulation.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: CS 2800, probability theory (e.g. BTRY 3080, ECON 3130, MATH 4710, ENGRD 2700), linear algebra (e.g. MATH 2940), calculus (e.g. MATH 1920) , programming proficiency (e.g. CS 2110), and CS 3780 or equivalent or permission of instructor.

• Imitation and interactive no-regret learning that handle distribution shifts, exploration/exploitation.
• Practical reinforcement learning leveraging both model predictive control and model-free methods.
• Learning perception models using probabilistic inference and 2D/3D deep learning.
• Frontiers in learning from human feedback (RLHF), planning with LLMs, human motion forecasting and offline reinforcement learning.

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