CS 4756 - Robot Learning

Fall. 4 credits. Letter grades only (no audit).

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. Co-meets with CS 5756.

S. Choudhury.

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.

Outcome 1: Imitation and interactive no-regret learning that handle distribution shifts, exploration/exploitation.

Outcome 2: Practical reinforcement learning leveraging both model predictive control and model-free methods.

Outcome 3: Learning perception models using probabilistic inference and 2D/3D deep learning.

Outcome 4: Frontiers in learning from human feedback (RLHF), planning with LLMs, human motion forecasting and offline reinforcement learning.