Johann discusses the complexities of imitation learning, particularly the confounding problem that arises when experts and imitators don't share the same data inputs. Using an example from autonomous driving, he illustrates how additional information, like a weather forecast, can influence human behavior, leading to inconsistencies in the data observed by the autonomous agent. This insight highlights the importance of understanding the context in which training data is generated.
