Introduction to Robotics, Spring 2020

Course number: CSE 525

Meets: Tuesday/Thursday 1:00-2:30pm

Location: Heavy Engineering Building LAB 201

Website: https://www3.cs.stonybrook.edu/~cse525 

Instructor: Michael S. Ryoo

Email: mryoo "at" cs.stonybrook.edu

Office: NCS 231

Office hours: Tuesday 2:30~4:00pm (+ by appointment)

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. 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:

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.

Prerequisites:

Interest in robot learning; basic programming skills (Python + TensorFlow?); neural network basics

Schedule:

Week

Topic

Slides

Week1

Introduction

Supervised action learning

Introduction

Supervised learning

Week2

Reinforcement learning introduction

Background

Dynamic programming

Week3

Vision - Geometry

RL - Monte Carlo methods

Monte Carlo

Week4

TD and Q-learning

TD

Deep Q learning

Week5

More TD

Final project 

Assignment #1

n-step TD

Week6

Policy gradient and Actor-Critic

Policy gradient

Week7

Planning

Model-based RL

Planning

Week8-9

Spring break

Week10

Midterm and project proposal presentation

Midterm

Course requirements and grading:

Programming assignments (30%).

Final project (40%): research project with proposal and presentation.

Midterm exam (10%).

FInal exam (20%).

Acknowledgement:

This course has been inspired by the Deep Reinforcement Learning course by Sergey Levine (Berkeley) and Reinforcement Learning course by Adam and Martha White (Alberta).