Artificial Intelligence for Robotics

Programming a Robotic Car

Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams. This class will teach…

Class Summary

Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars.

What Should I Know?

You should either already know Python, or have enough experience with another language to be confident you can pick up what you need on your own. Fortunately, Python was built to be easy to learn, read, and use. If you already know another programming language, you'll be coding in Python in less than an hour. Additionally, knowledge of probability and linear algebra will be helpful.

Python Review

Python for ProgrammersIntroduction to Programs Data Types and Variables
Python Lists
For Loops in Python
While Loops in Python
Writing a Simple Factorial Program
Fun with Strings

Probability

Basic ProbabilityProbability (Part 6) [Conditional Probability]
Probability (Part 7) [Bayes' Rule]
Probability (Part 8) [More Bayes' Rule]
Introduction to Random Variables
Probability Density Functions
Expected Value: E(X)

Linear Algebra

Introduction to Matrices
Matrix Multiplication (Part 1)
Matrix Multiplication (Part 2)
Inverse Matrix (Part 1)
Inverting Matrices (Part 2)
Inverting Matrices (Part 3)
Matrices to Solve a System of Equations
Singular Matrices
Introduction to Vectors
Vector Dot Product and Vector Length
Defining the Angle Between Vectors
Cross Product Introduction
Matrix Vector Products
Linear Transformations as Matrix Vector Products
Linear Transformation Examples: Scaling and Reflections
Linear Transformation Examples: Rotations in R2
Introduction to Projections
Exploring the Solution Set of Ax = b
Transpose of a Matrix
3x3 Determinant
Introduction to Eigenvalues and Eigenvectors

What Will I Learn?

This course will cover probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics.

Syllabus

Unit 1: Basics of probability

Monte-Carlo localization

Unit 2: Gaussians and continuous probability

Tracking other cars with Kalman filters

Unit 3: Car localization with particle filters

Unit 4: Planning and search

Determining where to drive with A* search

Unit 5: Controls

Controlling steering and speeds with PID

Unit 6: Putting it all together

Programming a self-driving car

Course Instructors

Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.