A world model is an AI system that learns a predictive representation of how an environment changes over time. It can use observations, actions, or language to estimate future states and possible outcomes before an agent acts. World models support planning in model-based reinforcement learning, robotics, embodied AI, autonomous driving, and interactive simulation. This site catalogs these systems with model records, research papers, benchmark context, and source links. Start with the world models database, compare systems on the leaderboard, or read the Performance Index methodology.
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AI systems designed to perceive, reason about and act in the physical world. World models are widely viewed as a foundational technology for physical AI, enabling robots, autonomous vehicles and industrial systems to plan in learned simulators.
Physical AI is a glossary concept in the applications layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Physical AI |
| Category | Applications |
| Definition | AI systems designed to perceive, reason about and act in the physical world. World models are widely viewed as a foundational technology for physical AI, enabling robots, autonomous vehicles and industrial systems to plan in learned simulators. |
| Related Models | 3 |
| Related Research | 2 |
| Model | Lab | Category |
|---|---|---|
| NVIDIA Cosmos | NVIDIA | Foundation World Model |
| V-JEPA 2 | Meta | Self-Supervised World Model |
| TD-MPC2 | MIT / Meta | Model-Based RL |
| Topic | Summary |
|---|---|
| World Models for Robotics | How world models improve robot learning, learned simulation, safe exploration, and sim-to-real transfer across manipulation, navigation, and control. |
| Foundation World Models | How foundation world models such as Cosmos and Genie 2 bring large-scale learned simulation to robotics, autonomous driving, and physical AI. |
| Term | Category | Definition |
|---|---|---|
| Embodied AI | Applications | AI systems that interact with and learn from the physical world through a body, whether a robot, drone, or virtual agent with physical constraints. World models are critical for embodied AI because they enable agents to predict and plan physical interactions. |
| Sim-to-Real Transfer | Applications | The process of transferring policies or skills learned in simulation to real-world robots or environments. World models help narrow the 'reality gap' by learning dynamics from real data rather than relying on hand-crafted simulators. |
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Published by world-models.io editorial board.
Lead editor Bernard Grenat.
This glossary page publishes stable definitions linked to related models, research topics, and primary-source context.
Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-09-27.
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