A Tutorial on World Models and Physical AI
Provides a conceptual framework for researchers and practitioners to understand and compare world modeling approaches in AI, but is a tutorial rather than a novel contribution.
This tutorial unifies explicit and implicit world model paradigms for physical AI, highlighting their role in prediction, reasoning, and decision-making for robotics and autonomous driving, while noting challenges in hierarchical reasoning and long-horizon planning.
World modeling is emerging as a central principle for building intelligent systems capable of prediction, reasoning, and decision making. A central distinction can be drawn between explicit world models, which learn structured dynamics for rollout-based reasoning and planning, and implicit world models, which encode predictive structure within scalable learned representations. These complementary paradigms provide a foundation for physical AI in domains such as robotics and autonomous driving, enabling intelligence beyond reactive control under real-world constraints. Recent foundation models further suggest a pathway toward unified systems integrating perception, prediction, and action. Despite rapid progress, major challenges remain in hierarchical reasoning, long-horizon planning, and autonomous goal formation, which are critical for advancing toward artificial general intelligence. This tutorial presents a coherent framework in which diverse world modeling approaches are unified through shared predictive structure and differentiated by how such structure is represented and exploited.