ECE598SG Special Topics in Learning-based Robotics (Fall 2021)Instructor: Saurabh Gupta Office Hours | Campuswire (announcements and discussion) | Lecture Videos (choose Log In Via Institution) To the extent possible, you should use Campuswire for asking questions. If you have a logistics question specific to yourself, you can email the course staff at rl-fa21-staff@lists.illinois.edu. Access to Campuswire: If you are registered for the class, you should have received a invite to Campuswire. If you haven't, please drop us an email at rl-fa21-staff@lists.illinois.edu. Course DescriptionThis course will introduce students to recent developments in the area of robot learning. The course will start with an overview of background material from relevant subfields: computer vision, machine learning, robotics and control theory. This will be followed by advanced techniques (model-free reinforcement learning with function approximators, model learning, model-based reinforcement learning with learned models, imitation learning, inverse reinforcement learning, self-supervised learning, exploration, hierarchies) in this area. 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. Learning OutcomesAfter taking this course you will be able to:
PrerequisitesThis is an advanced gradate course aimed at graduate students conducting research in relevant research areas. The course will be structured around research papers published within the last few years in computer vision, robotics, and machine learning. Students should be familiar with reading and critiquing research papers, and should have a basic understanding of concepts in artificial intelligence, and machine learning. Students must have taken at least one of the following (or equivalent) courses: ECE 448 / CS 440 (Introduction to Artificial Intelligence), ECE 544NA (Pattern Recognition), ECE 549 / CS 543 (Computer Vision). If you are not sure whether you meet the prerequisites, talk to the instructor after the first class or in office hours. Recommended Textbooks
Grading SchemeTentative and subject to change.
AcknowledgementWe thank the many researchers who have made their slides and course materials available. |