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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.
Sim-to-Real Transfer is a glossary concept in the applications layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Sim-to-Real Transfer |
| Category | Applications |
| Definition | 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. |
| Related Models | 3 |
| Related Research | 1 |
| Model | Lab | Category |
|---|---|---|
| NVIDIA Cosmos | NVIDIA | Foundation World Model |
| UniSim | Google DeepMind | Generative 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. |
| Term | Category | Definition |
|---|---|---|
| Reality Gap | Concepts | 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. |
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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: 2026-06-21.
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