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A training technique that randomizes simulator parameters (textures, lighting, friction, dynamics) so that policies generalize to the real world. Often combined with learned world models to expand the distribution of training scenarios beyond what real data alone can provide.
Domain Randomization is a glossary concept in the training layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Domain Randomization |
| Category | Training |
| Definition | A training technique that randomizes simulator parameters (textures, lighting, friction, dynamics) so that policies generalize to the real world. Often combined with learned world models to expand the distribution of training scenarios beyond what real data alone can provide. |
| 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. |
| 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. |
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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.
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