---
title: "Reality Gap | Glossary | world-models.io"
canonical: "https://world-models.io/en/glossary/reality-gap/"
description: "Definition of reality gap: the discrepancy between simulated and real-world environments that causes sim-to-real failures."
author: "Bernard Grenat"
generated: "2026-10-30"
---

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The Knowledge Hub for AI World Models

# Reality Gap

The discrepancy between simulated and real-world environments that causes policies trained in simulation to fail when deployed on physical systems.

robotics

model-based-rl

simulation

embodied-ai

## What is a world model?

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](https://world-models.io/en/world-models-database/), compare systems on the [leaderboard](https://world-models.io/en/leaderboard/), or read the [Performance Index methodology](https://world-models.io/en/methodology/performance-index/).

## What Is Reality Gap?

Editorial definition preserved directly in static HTML.

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.

## Term Snapshot

Static glossary definition snapshot for crawlers and no-JS readers.

| 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 |

## Related Models

| Model | Lab | Category |
| --- | --- | --- |
| [NVIDIA Cosmos](https://world-models.io/en/models/nvidia-cosmos/) | NVIDIA | Foundation World Model |
| [UniSim](https://world-models.io/en/models/unisim/) | Google DeepMind | Generative World Model |

## Related Research

| Topic | Summary |
| --- | --- |
| [World Models for Robotics](https://world-models.io/en/research/world-models-robotics/) | How world models improve robot learning, learned simulation, safe exploration, and sim-to-real transfer across manipulation, navigation, and control. |
| [AI Simulation Systems](https://world-models.io/en/research/ai-simulation-systems/) | How AI simulation systems and learned simulators reduce the reality gap and extend or replace hand-crafted engines for autonomous agents. |

## Related Terms

| Term | Category | Definition |
| --- | --- | --- |
| [Sim-to-Real Transfer](https://world-models.io/en/glossary/sim-to-real/) | 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. |

## Quick Answer

Short extractable summary preserved directly in static HTML.

- Reality Gap is a glossary concept used across world-models.io to clarify language, methods, and architectural ideas in the field.
- Use this page to get the definition quickly, then continue into related models, research topics, and adjacent terms for context.

## Editorial Trust Signals

Editorial provenance and refresh policy preserved directly in static HTML.

**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.

Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. [See the editorial policy.](https://world-models.io/en/editorial-policy/)

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## Reference Sources

Primary sources related to this term, surfaced directly in static HTML.

- [NVIDIA Cosmos paper](https://arxiv.org/abs/2501.03575) NVIDIA primary research source
- [NVIDIA Cosmos official page](https://www.nvidia.com/en-us/ai/cosmos/) NVIDIA official page
- [UniSim paper](https://arxiv.org/abs/2310.06680) Google DeepMind primary research source
- [AI Simulation Systems reference](https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/) Google DeepMind, 2024. Genie 2.