ECE598SG Special Topics in Learning-based Robotics (Fall 2020)

Instructor: Saurabh Gupta
TA: Rishabh Goyal (rgoyal6)
Lecture Times: Tuesday / Thursday 3:30PM - 4:50PM
Lecture Location: Zoom Meeting ID: 99531780977, password: 111000. Illinois login needed.

Office Hours | Piazza (announcements and discussion) | Lecture Videos (choose Log In Via Institution)

To the extent possible, you should use Piazza for asking questions. If you have a logistics question specific to yourself, you can email the course staff at rl-fa20-staff@lists.illinois.edu.

Course Description

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 sub-fields: computer vision, machine learning, reinforcement learning, control theory and robotics. This will be followed by discussion of advanced techniques for arriving at policies for robots, such as model learning, model-based RL with learned models, imitation learning, inverse reinforcement learning, self-supervised learning, exploration, and hierarchical reinforcement learning. Finally, we will consider applications of these concepts to robot navigation and manipulation. The course will cover these topics through discussion of recent publications. The course will also include programming assignments, and open-ended project work that will provide students a flavor of how to conduct research in this emerging area.

Learning Outcomes

After taking this course you will be able to:

1. Understand basic techniques for learning policies for robots.

2. Understand and appreciate recent literature in robot learning.

3. Design approaches to learn policies for solving various robotic tasks.

4. Design experiments or conduct analysis to validate approaches.

Prerequisites

This 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

1. Reinforcement Learning: An Introduction. Richard S. Sutton and Andrew G. Barto. Second Edition, MIT Press, Cambridge, MA, 2018. Available online.

2. Computer Vision: Algorithms and Applications. Richard Szeliski, Microsoft Research. Available online.

3. Robotic Systems. Kris Hauser. Draft available online.

4. Modern Robotics: Mechanics, Planning, and Control. Frank C. Park, Kevin M. Lynch. Cambridge University Press. Available Online.

Grading Scheme

Tentative and subject to change.

1. Paper Reviews: 20%. More details here.

2. Programming assignments: 35%. 3-4 MPs, to be done individually.

3. Final project: 35%. More details here.

4. Participation: 10% Participation in in-class discussion, and on Piazza.

Acknowledgement

We would like to thank the many researchers who have made their slides and course materials available.