CS 5756

Robot Learning

Course Description

Course information provided by the 2026-2027 Catalog.

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.

Prerequisites background equivalent to: CS 2800; STSCI 3080 or ECON 3130 or MATH 4710 or ENGRD 2700; MATH 2940; MATH 1920; CS 2110.

Enrollment Information Enrollment limited to: Computer Science (CS) Master of Engineering (M.Eng.) students.

Last 4 Terms Offered 2026SP, 2025SP, 2024FA, 2024SP

Learning Outcomes

• 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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