[MUSIC] Welcome to the Robotics Estimation and Learning Course. This is one of the modules in this robotics specialization series. This course will teach you how robots can estimate properties of the world from observations over time, and learn from their prior experience. 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. What do we mean by estimation and learning? By estimation, we mean to estimate some aspect of the state of the world from noisy, incomplete and uncertain data. And by learning, we mean to use, have the robots use prior experience to improve their performance under this uncertainty. What are the sources of uncertainty in robotics? One, there are a sensor noise. That is that the sensors can provide inaccurate information. Two, there could be a lack of knowledge about the world. That is, things can be hidden from view or that the robots could not perceive. And three, there might be dynamic changes in the motion and in the environment. That is, that things maybe moving over time and the robots do not know exactly where things are at the current instant. So there are several ways to deal with this uncertainty. In this course, we'll focus on two different aspects of dealing with this uncertainty. The first is probabilistic modeling. That is, using probability distributions to account for this uncertainty. And the second method is by using machine learning, to learn from previous experience to be able to predict the future uncertain world. The core structure is as follows, over the next four weeks you'll be learning four different topics. In the first week, we will be focused on Gaussian Model Learning. That is using a Gaussian Model to represent the probability distribution over potential states. Then you will learn how to use this Gaussian Model to do maximum likelihood learning. In the second week, we will focus on Kalman Filtering. That is how to model in a probabilistic manner a dynamic world. And in the third week, you will be doing Robot Mapping, using probabilistic techniques to map out the surrounding environment around the robot. And in the fourth week you will learn about robot localization using particle filters. That will incorporate different aspects of sensor information to keep track of the robot's pose over time.
