CS 4756
Robot Learning
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.
Topics
- 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.
Official roster learning outcomes
Prerequisites & course information
CS 2800; STSCI 3080 or ECON 3130 or MATH 4710 or ENGRD 2700; MATH 2940; MATH 1920; CS 2110.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Robot Learning · 4.0 · Undergraduate