Sergey discusses the distinction between causal and correlative models, emphasizing the importance of understanding causality in robotics. He reflects on his initial doubts about its significance, explaining that while predictive models can make mistakes, the ability to adapt and update based on real-world interactions is crucial for effective decision-making. This insight highlights the necessity of causality for driving meaningful change in robotic applications.
