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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.
Embodied AI is a glossary concept in the applications layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Embodied AI |
| Category | Applications |
| Definition | 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. |
| 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 |
|---|---|---|
| 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. |
| Foundation Model | Paradigms | A large-scale model trained on broad data that can be adapted to many downstream tasks. Foundation world models (like NVIDIA Cosmos and Genie 2) learn general-purpose representations of world dynamics, analogous to how GPT models serve as foundations for language tasks. |
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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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