CS 6758
Deep Learning for Robotics
Deep learning has become a pivotal force in recent robotics research advancements, from estimating the state of the world to solving complex long-horizon tasks. The new paradigm shifts from traditional feature and model engineering to learning task-relevant representations from raw data. This is fueled by increasingly more affordable hardware and diverse data sources from which algorithms may learn from. This graduate-level course examines how deep learning approaches have been applied to robotics problems, including various topics of robot perception and control. We will also discuss the recent trend of large-scale representation learning and foundation models for robotics.
Topics
- Evaluate recent works on deep robot learning.
- Demonstrate how deep learning methods can be utilized for perception and control.
- Compare data-driven approaches and tradition approaches and describe their strengths and weaknesses.
- Implement, evaluate, and analyze cutting-edge deep robot learning methods.
- Apply deep learning techniques to solve real-world robot applications.
Official roster learning outcomes
Sources
- Course catalogue ↗2026-2027