Robots are never certain. This chapter builds the probabilistic toolkit used in every later chapter: describing uncertainty, modeling sensors, updating beliefs with Bayes’ rule, and choosing actions that minimize expected cost.
| Section | What you will learn |
|---|---|
| 2.1 Probability for Robots | PMFs, Gaussians, sampling |
| 2.2 Sensors and Noise | sensor models and likelihoods |
| 2.3 Bayesian Inference | Bayes’ rule, MAP, Gaussian fusion |
| 2.4 Decisions Under Uncertainty | expected cost and value of information |