### Top 50 MOOCs

To support our site, Class Central may be compensated by some course providers.

# Artificial Intelligence for Robotics

17323
students interested

Taken this course? Share your experience with other students. Write review

## Overview

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.

This course is offered as part of the Georgia Tech Masters in Computer Science. The updated course includes a final project, where you must chase a runaway robot that is trying to escape!

Why Take This Course?

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

At the end of the course, you will leverage what you learned by solving the problem of a runaway robot that you must chase and hunt down!

## Syllabus

### Lesson 1: Localization

• Localization
• Total Probability
• Uniform Distribution
• Probability After Sense
• Normalize Distribution
• Phit and Pmiss
• Sum of Probabilities
• Sense Function
• Exact Motion
• Move Function
• Bayes Rule
• Theorem of Total Probability

### Lesson 2: Kalman Filters

• Gaussian Intro
• Variance Comparison
• Maximize Gaussian
• Measurement and Motion
• Parameter Update
• New Mean Variance
• Gaussian Motion
• Kalman Filter Code
• Kalman Prediction
• Kalman Filter Design
• Kalman Matrices

### Lesson 3: Particle Filters

• Slate Space
• Belief Modality
• Particle Filters
• Using Robot Class
• Robot World
• Robot Particles

### Lesson 4: Search

• Motion Planning
• Compute Cost
• Optimal Path
• First Search Program
• Expansion Grid
• Dynamic Programming
• Computing Value
• Optimal Policy

### Lesson 5: PID Control

• Robot Motion
• Smoothing Algorithm
• Path Smoothing
• Zero Data Weight
• Pid Control
• Proportional Control
• Implement P Controller
• Oscillations
• Pd Controller
• Systematic Bias
• Pid Implementation
• Parameter Optimization

### Lesson 6: SLAM (Simultaneous Localization and Mapping)

• Localization
• Planning
• Segmented Ste
• Fun with Parameters
• SLAM
• Graph SLAM
• Implementing Constraints
• Matrix Modification
• Untouched Fields
• Landmark Position
• Confident Measurements
• Implementing SLAM

Sebastian Thrun

## Reviews for Udacity's Artificial Intelligence for Robotics 4.8 Based on 23 reviews

• 5 stars 78%
• 4 stars 22%
• 3 star 0%
• 2 star 0%
• 1 star 0%

Did you take this course? Share your experience with other students.

• 1
Anonymous
5.0 2 years ago
completed this course.
Wonderfully taught! Very elegant building of concepts from minimal discourse. "Show me, don't tell me" leads to deep understanding naturally. Very gifted teacher.
Anonymous
5.0 6 years ago
completed this course.
I only did the first chapter as I was looking for localization algorithms and I thought it was really useful.
1 person found
Jordan L
5.0 5 months ago
by completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
Easily one of the very best MOOCs I have ever taken. Sebastian Thrun brings an incredible eagerness to the topic and his examples are very well prepared. He dives right into the key topics of AI for robotics with great toy examples, allowing a newbie to quickly wrap their minds around the system. Then he scales up the problem a bit to add complexity, building up the complexity at a fair pace. At the end of each task you feel like you have accomplished something great.

Also, he pre-writes all the auxiliary code so you can focus on the core important code, not unnecessary plumbing.
Anonymous
5.0 6 years ago
completed this course.
Pretty good course. I did not finish it because it overlapped a lot with the first version that Sebastian Thrun did together with Peter Norvig. From the first several units I got an impression that the course is an aggregation of loosely connected topics (as if the authors tried to cover a lot more than they had time for), but nevertheless each topic is well explained.
Anonymous
5.0 9 months ago
completed this course.
useful course for control and automation engineering and researcher

focusing in soft computing and controller design problem and introduce new techniques in control system design
Federico R
5.0 a year ago
by is taking this course right now, spending 2 hours a week on it and found the course difficulty to be medium.
This course provides a comprehensive overview of the AI techniques used for localization and navigation of mobile robots. Exactly what I was looking for.
Mal M
5.0 4 years ago
by completed this course, spending 4 hours a week on it and found the course difficulty to be hard.
3 people found
Anonymous
5.0 6 years ago
completed this course.
excellent, clear and easy to understand for people with some programming and math skills
Maxime L
5.0 4 years ago
by completed this course.
1 person found
Julio M
4.0 2 years ago
by completed this course.
Rey C
5.0 4 years ago
by completed this course and found the course difficulty to be easy.
0 person found
Franta P
4.0 3 years ago
by completed this course.
Alvaro C
5.0 3 years ago
by completed this course.
Vikram P
4.0 3 years ago
is taking this course right now.
Liviu L
5.0 3 years ago
by completed this course.
Wojciech C
4.0 3 years ago
completed this course.
Ciprian C
5.0 3 years ago
completed this course.
Mauro L
4.0 3 years ago
by completed this course.
Johan K
5.0 3 years ago
by completed this course.
Ryan R
5.0 2 years ago
by completed this course.