CSE 481C: Robotics Capstone. Students work in teams to design and implement algorithms for robotic perception and control.
CSE 478: Autonomous 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 542: Reinforcement Learning. This course is directed to graduate students who want to build adaptive software that interacts with the world. Although much of the material will be driven by robotics applications, anyone interested in applying learning to decision-making or an interest in complex adaptive systems is welcome.
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.
CSE 599G: Deep Robotic Learning. In this course, we examine how we can leverage deep learning methods to build robotic learning systems that can adapt and continue improving in real world applications. This course aims to provide an understanding of how deep learning methods can be useful for robotics, with an in depth look into research frontiers and representative papers. We plan to cover a wide range of methods: reinforcement learning in model-based and model-free settings, imitation learning and offline reinforcement learning, multi-task and meta learning, transfer learning in robotics and many more topics at the frontier of robotic learning.