Chelsea discusses the distinction between learned models and known models in robotics, emphasizing the importance of model-based control. She explains how predictions can be made not just in pixel space but through latent representations, allowing for a more efficient approach in reinforcement learning tasks. The conversation highlights the relevance of these methods in real-world applications where models are not predefined.
