Evaluating World Models in Robotics

This chapter explores various methods for evaluating world and policy models in robotics, including benchmarks for tasks like RGB prediction and depth evaluation. It discusses the impact of video diffusion models on robot training data generation and the balance between real-world data and synthetic data for effective training. The chapter also addresses the complexities and trade-offs in generating imagined scenarios for robotic tasks, emphasizing the need for improved controllability in future advancements.