CSE 481C: Robotics Capstone. Students work in teams to design and implement algorithms for robotic perception and control.
CSE 490R: Robotics. This course covers topics related to state estimation (particle filters, motion models, sensor models, etc), planning/control (search-based planners, lattice-based planners, trajectory-following techniques, etc), and perception and learning (object detection, learning from demonstrations, etc.). Course concepts will culminate in a partially open-ended final project with a final demo on 1/10th-sized rally cars.
CSE 571: Probabilistic Robotics. This course introduces various techniques for Bayesian state estimation and its application to problems such as robot localization, mapping, and manipulation. The course will also provide a problem-oriented introduction to relevant machine learning and computer vision techniques.