Sergey discusses the complexities of imitation learning, particularly how adding more sensors can lead to causal confusion and degrade system performance. He highlights the importance of understanding the underlying correlations that can arise, using real-world examples like autonomous driving. By formalizing these issues, he aims to provide insights that could help practitioners avoid pitfalls in their implementations.
