Two, there could be a lack of knowledge about the world. That is that the sensors can provide inaccurate information. What are the sources of uncertainty in robotics? One, there are a sensor noise. And by learning, we mean to use, have the robots use prior experience to improve their performance under this uncertainty. 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. This course will teach you the underlying mathematical framework and the computational algorithms that the robots are using to do these tests. 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. In order to execute an appropriate dive to save the ball from going into the goal.

Then as the ball approaches the goal, the goalie robot needs to estimate the speed and direction of the ball. In this scenario, the attacking robot needs to estimate where the ball and the goal are located in order to line up a kick. Plan their behaviors to either attack or defend, and send motor commands for locomotion and to manipulate the orange soccer ball in various ways. 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. Here our humanoid robot soccer team is playing in a match with another team at the RoboCup competition. Why do robots need to estimate and learn? Consider the following robot soccer example. And how to implement probabilistic algorithms to account for this uncertainty.

Over the next four weeks, you will learn various methods to deal with noise and uncertainty in real robots. This course will teach you how robots can estimate properties of the world from observations over time, and learn from their prior experience. This is one of the modules in this robotics specialization series. Welcome to the Robotics Estimation and Learning Course.

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