CS 6758 - Deep Learning for Robotics

Fall. 4 credits. Student option grading.

K. Fang.

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.

Outcome 1: Evaluate recent works on deep robot learning.

Outcome 2: Demonstrate how deep learning methods can be utilized for perception and control.

Outcome 3: Compare data-driven approaches and tradition approaches and describe their strengths and weaknesses.

Outcome 4: Implement, evaluate, and analyze cutting-edge deep robot learning methods.

Outcome 5: Apply deep learning techniques to solve real-world robot applications.