Over the next four weeks, you will learn various methods to deal with noise and

uncertainty in real robots.

And how to implement probabilistic algorithms to account for

this uncertainty.

Let's get started.

Why do robots need to estimate and learn?

Consider the following robot soccer example.

Here our humanoid robot soccer team is playing in a match with another team

at the RoboCup competition.

The robots are complete autonomous and their onboard computers need to integrate

information from the inertial and vision sensors to perceive the world around them.

Plan their behaviors to either attack or defend, and send motor commands for

locomotion and to manipulate the orange soccer ball in various ways.

In this scenario, the attacking robot needs to estimate where the ball and

the goal are located in order to line up a kick.

Then as the ball approaches the goal,

the goalie robot needs to estimate the speed and direction of the ball.

In order to execute an appropriate dive to save the ball from going into the goal.

In order to accomplish this accurately and efficiently, the robots need to learn

the appropriate parameters during the many hours of practice before the match.

This course will teach you the underlying mathematical framework and

the computational algorithms that the robots are using to do these tests.