From outer space to abandoned mines to urban disaster areas, robotic agents are at the forefront of exploration in hostile environments. To successfully complete these missions, a robot must be able to operate for extended periods of time with little or no input from human controllers; this includes the ability to gracefully handle uncertainty and error. In this class, we'll see how to use the theory of probability to build robots that are better, smarter, and more resistant to uncertainty and error, with a heavy emphasis on real-world examples (e.g., rockets, self-driving cars, rescue robots, and perhaps a submarine or two).