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# Artificial Intelligence for Robotics

## 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
Runaway Robot Final Project

Sebastian Thrun

## Reviews

4.8 rating, based on 23 reviews

Start your review of Artificial Intelligence for Robotics

• Jordan Lui 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

Anonymous 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

Anonymous completed this course.

excellent, clear and easy to understand for people with some programming and math skills
• Federico Renda 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.
• Anonymous

Anonymous completed this course.

I only did the first chapter as I was looking for localization algorithms and I thought it was really useful.
• Anonymous

Anonymous 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.
• Mal Minhas completed this course, spending 4 hours a week on it and found the course difficulty to be hard.

• Anonymous

Anonymous 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
• Maxime Leclerc completed this course.

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• Vikram Pawar
2

Vikram Pawar is taking this course right now.

• Julio Vizcaíno Molina completed this course.

• Rey Raul Coaguila completed this course and found the course difficulty to be easy.

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• Liviu completed this course.

• Ciprian Dan Chirilă
1

Ciprian Dan Chirilă completed this course.

• Wojciech Czarnecki
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Wojciech Czarnecki completed this course.

• Oleg Tyshev completed this course.

• Alvaro Carrera completed this course.

• Franta Polach completed this course.

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