CS 5756 - Robot Learning

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

Prerequisite: CS 2800, probability theory (e.g. BTRY 3010, 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 4756.

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.