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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The discrepancy between simulated and real-world environments that causes policies trained in simulation to fail when deployed on physical systems. Learned world models trained on real-world data help reduce this gap compared to hand-crafted simulators.
Reality Gap is a glossary concept in the concepts layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Reality Gap |
| Category | Concepts |
| Definition | The discrepancy between simulated and real-world environments that causes policies trained in simulation to fail when deployed on physical systems. Learned world models trained on real-world data help reduce this gap compared to hand-crafted simulators. |
| Related Models | 2 |
| Related Research | 2 |
| Model | Lab | Category |
|---|---|---|
| NVIDIA Cosmos | NVIDIA | Foundation World Model |
| UniSim | Google DeepMind | Generative World Model |
| 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. |
| AI Simulation Systems | How AI simulation systems and learned simulators reduce the reality gap and extend or replace hand-crafted engines for autonomous agents. |
| Term | Category | Definition |
|---|---|---|
| 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.
Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.
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