Introduction to Robotics, Spring 2020
Course number: CSE 525
Meets: Tuesday/Thursday 1:00-2:30pm
Location: Heavy Engineering Building LAB 201 Google Meet
Instructor: Michael S. Ryoo
Email: mryoo "at" cs.stonybrook.edu
Office: NCS 231
Office hours: Wednesday 10-11am (+ by appointment), virtually
Building an intelligent robot operating robustly in everyday environment has been one of the ultimate objectives in artificial intelligence. In this course, the students will learn fundamental algorithms for robot learning and perception. The course will also focus on recent progress in deep reinforcement learning for robotics, providing the students an opportunity to learn about state-of-the-art robot learning approaches. This is a research-oriented course composed of a series of lectures by the instructor. Some basic deep representation learning concepts will occasionally be discussed, in order to provide the right background to the students.
The topics to cover includes:
There is no particular textbook and the course will rely heavily on the lecture slides and the state-of-the-art papers. For RL basics, Sutton and Barto’s book is highly recommended.
Interest in robot learning; basic programming skills (Python + TensorFlow?); neural network basics
Supervised action learning
Reinforcement learning introduction
Vision - Geometry (by Dimitris)
RL - Monte Carlo methods
TD and Q-learning
Policy gradient and Actor-Critic
Midterm and project proposal presentation
Model-based RL (2)
Visual representations for robots
Camera and stereo
Depth and ego-motion (visual odometry)
Tracking (by Haibin)
Robot learning at NASA-JPL (by Renaud)
Course requirements and grading:
Programming assignments (30%).
Final project (60%): research project with proposal and presentation.
Midterm exam (10%).
This course has been inspired by the Deep Reinforcement Learning course by Sergey Levine (Berkeley), Reinforcement Learning course by Adam and Martha White (Alberta), Computer Vision course by Linda Shapiro (UW).