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Dec 02, 2024
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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.
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