CS 598 SG

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

This course will introduce students to recent developments in the area of learning-based robotics. The course will start with an overview of background material from relevant subfields: computer vision, machine learning, robotics and control theory. Next, we will discuss advanced techniques for learning policies for robots, such as model-free reinforcement learning with function approximators, model learning, model-based RL with learned models, imitation learning, inverse reinforcement learning, self-supervised learning, exploration, and hierarchical reinforcement learning. These advanced techniques will be covered via recent research papers that develop and validate them. The course will conclude with case-studies on robotic navigation, and manipulation from recent papers. Project work as part of the course will provide a flavor of research in this new emerging area. Prerequisites: Understanding of basic concepts in artificial intelligence, and machine learning. Students must have tak