A Tutorial on World Models and Physical AI

• URL: http://arxiv.org/abs/2606.12783v1
• Date: Thu, 11 Jun 2026 00:52:39 GMT
• Title: A Tutorial on World Models and Physical AI
• Abstract summary: World modeling is emerging as a central principle for building intelligent systems capable of prediction, reasoning, and decision making.<n>This tutorial presents a coherent framework in which diverse world modeling approaches are unified through shared predictive structure.
• Score: 0.30458514384586394
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• World Action Models: The Next Frontier in Embodied AI [123.5787299299832]
  Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning.<n>They learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention.<n>A growing body of work addresses this limitation by integrating world models, predictive models of environment dynamics, into the action generation pipeline.
  arXiv  Detail & Related papers  (2026-05-12T13:10:52Z)
• The Trinity of Consistency as a Defining Principle for General World Models [106.16462830681452]
  General World Models are capable of learning, simulating, and reasoning about objective physical laws.<n>We propose a principled theoretical framework that defines the essential properties requisite for a General World Model.<n>Our work establishes a principled pathway toward general world models, clarifying both the limitations of current systems and the architectural requirements for future progress.
  arXiv  Detail & Related papers  (2026-02-26T16:15:55Z)
• A Step Toward World Models: A Survey on Robotic Manipulation [58.8419978790227]
  We look at approaches that exhibit the core capabilities of world models through a review of methods in robotic manipulation.<n>We analyze their roles across perception, prediction, and control, identify key challenges and solutions, and distill the core components, capabilities, and functions that a fully realized world model should possess.
  arXiv  Detail & Related papers  (2025-10-31T00:57:24Z)
• World Models Should Prioritize the Unification of Physical and Social Dynamics [57.91940497010114]
  This paper argues that the systematic, bidirectional unification of physical and social predictive capabilities is the next crucial frontier for world model development.<n>We contend that comprehensive world models must holistically integrate objective physical laws with the subjective, evolving, and context-dependent nature of social dynamics.
  arXiv  Detail & Related papers  (2025-10-24T07:42:37Z)
• Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges [87.02855999212817]
  Edge General Intelligence (EGI) represents a transformative evolution of edge computing, where distributed agents possess the capability to perceive, reason, and act autonomously.<n>World models act as proactive internal simulators that not only predict but also actively imagine future trajectories, reason under uncertainty, and plan multi-step actions with foresight.<n>This survey bridges the gap by offering a comprehensive analysis of how world models can empower agentic artificial intelligence (AI) systems at the edge.
  arXiv  Detail & Related papers  (2025-08-13T07:29:40Z)
• World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks [55.90051810762702]
  We present a comprehensive overview of world models, highlighting their architecture, training paradigms, and applications across prediction, generation, planning, and causal reasoning.<n>We propose Wireless Dreamer, a novel world model-based reinforcement learning framework tailored for wireless edge intelligence optimization.
  arXiv  Detail & Related papers  (2025-05-31T06:43:00Z)
• Possible Principles for Aligned Structure Learning Agents [2.38041389056271]
  A possible path toward scalable aligned AI rests upon enabling artificial agents to learn a good model of the world that includes a good model of our preferences.<n>We discuss the essential role of core knowledge, information geometry and model reduction in structure learning.<n>As an illustrative example, we mathematically sketch Asimov's Laws of Robotics, which prescribe agents to act cautiously to minimize the ill-being of other agents.
  arXiv  Detail & Related papers  (2024-09-30T22:06:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.