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This tutorial unifies explicit and implicit world modeling paradigms as foundational components for physical AI systems in robotics and autonomous driving. It addresses critical hurdles such as hierarchical reasoning and long-horizon planning necessary to advance toward artificial general intelligence beyond reactive control mechanisms. The work provides a coherent framework essential for researchers developing predictive architectures that integrate perception, prediction, and action under real-world constraints.
A Tutorial on World Models and Physical AI
arXiv:2606.12783v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI)
arXiv:2606.12783 [cs.AI]
(or arXiv:2606.12783v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.12783 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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