Introduction to Robotics, Fall 2023

Course number: CSE 378

Meets: Tuesday/Thursday 1-2:20pm

Location: FREY HALL 211 (in-person)

Website: http://michaelryoo.com/course/cse378/ 

Instructor: Michael S. Ryoo

Email: mryoo "at" cs.stonybrook.edu

Office: NCS 231

Office hours: TBD

TA: Jinghuan Shang jishang@cs.stonybrook.edu 

TA office hours: TBD

Course description:

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, as well as basics to better understand them. 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. The course is 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:

• Robot reinforcement learning

• Dynamic programming
• TD (Q-Learning)
• Policy gradients
• Model-based RL and planning
• Transformers for robots
• Large language models for robots

• Vision for Robotics

• Camera and stereo
• Depth and ego-motion
• Localization and mapping
• Recognition in the wild

Textbooks:

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. For CV basics, check Szeliski’s textbook.

Prerequisites:

Interest in robot learning; basic programming skills (Python + PyTorch?); Machine Learning (e.g., neural networks) basics

Schedule:

Use your @stonybrook.edu account to access the slides.

Course requirements and grading:

Programming assignments (40%).

Final exam (30%).

Midterm exam (20%).

Acknowledgement:

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).