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AI systems designed to perceive, reason about and act in the physical world. World models are widely viewed as a foundational technology for physical AI, enabling robots, autonomous vehicles and industrial systems to plan in learned simulators.
Physical AI is a glossary concept in the applications layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Physical AI |
| Category | Applications |
| Definition | AI systems designed to perceive, reason about and act in the physical world. World models are widely viewed as a foundational technology for physical AI, enabling robots, autonomous vehicles and industrial systems to plan in learned simulators. |
| Related Models | 3 |
| Related Research | 2 |
| Model | Lab | Category |
|---|---|---|
| NVIDIA Cosmos | NVIDIA | Foundation World Model |
| V-JEPA 2 | Meta | Self-Supervised 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. |
| Foundation World Models | How foundation world models such as Cosmos and Genie 2 bring large-scale learned simulation to robotics, autonomous driving, and physical AI. |
| Term | Category | Definition |
|---|---|---|
| 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. |
| 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.
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