This paper surveys Generative Physical Artificial Intelligence (GPAI) systems, which integrate large-scale foundation models with physical embodiments for robotics applications. It introduces a taxonomy of five approaches, including Robot Foundation Models, Vision-Language Action models, and Diffusion Policy Models, and discusses their complementary relationships and research directions. This is relevant to people building AI agents as it highlights advancements and limitations in GPAI, which may inform the development of future AI systems.