--- title: "Embodied AI | Glossary | world-models.io" canonical: "https://world-models.io/en/glossary/embodied-ai/" description: "Definition of embodied AI: AI systems that interact with and learn from the physical world through a body." 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 # Embodied AI AI systems that interact with and learn from the physical world through a body. World models are critical for embodied AI because they enable prediction and planning. 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 Embodied AI? Editorial definition preserved directly in static HTML. 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. ## Term Snapshot Static glossary definition snapshot for crawlers and no-JS readers. | 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 | ## Related Models | Model | Lab | Category | | --- | --- | --- | | [NVIDIA Cosmos](https://world-models.io/en/models/nvidia-cosmos/) | NVIDIA | Foundation World Model | | [UniSim](https://world-models.io/en/models/unisim/) | Google DeepMind | Generative 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. | ## Related Terms | Term | Category | Definition | | --- | --- | --- | | [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. | | [Foundation Model](https://world-models.io/en/glossary/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. | ## Quick Answer Short extractable summary preserved directly in static HTML. - Embodied 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 - [UniSim paper](https://arxiv.org/abs/2310.06680) Google DeepMind primary research source - [TD-MPC2 paper](https://arxiv.org/abs/2310.16828) MIT / Meta primary research source