--- title: "Physical AI | World Models Glossary | world-models.io" canonical: "https://world-models.io/en/glossary/physical-ai/" description: "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." author: "Bernard Grenat" generated: "2026-10-30" --- New: the Timeline is live. Track world model releases, papers, and benchmark updates in real time. The Knowledge Hub for AI World Models # Physical AI 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. 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 Physical AI? Editorial definition preserved directly in static HTML. 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. ## Term Snapshot Static glossary definition snapshot for crawlers and no-JS readers. | 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 | ## Related Models | Model | Lab | Category | | --- | --- | --- | | [NVIDIA Cosmos](https://world-models.io/en/models/nvidia-cosmos/) | NVIDIA | Foundation World Model | | [V-JEPA 2](https://world-models.io/en/models/v-jepa-2/) | Meta | Self-Supervised World Model | | [TD-MPC2](https://world-models.io/en/models/td-mpc2/) | MIT / Meta | Model-Based RL | ## 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. | | [Foundation World Models](https://world-models.io/en/research/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. | ## Related Terms | Term | Category | Definition | | --- | --- | --- | | [Embodied AI](https://world-models.io/en/glossary/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](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. - Physical AI 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/) Primary sources only Last reviewed date visible Methodology documented Source links included Correction channel published Review date visible ## 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 - [V-JEPA 2 paper](https://arxiv.org/abs/2506.09985) Meta primary research source - [V-JEPA 2 official page](https://ai.meta.com/research/vjepa/) Meta official page