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, compare systems on the leaderboard, or read the Performance Index methodology.
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| Attribute | Value |
|---|---|
| Model | RT-2 |
| Lab / Organization | DeepMind |
| Category | Foundation World Model |
| Subtype | Vision-Language-Action Model |
| World Model Type | Web-knowledge transfer model for robotics |
| Primary Domain | Robotics |
| Architecture | PaLI-X / PaLM-E backbone fine-tuned with action tokenization |
| Modality | Visual + Language + Proprioceptive |
| Training Method | Co-fine-tuning on web VLM data + robot demonstration trajectories |
| Status | active |
| Year | 2023 |
| Performance Index | 72/100 (medium confidence, v1.1) |
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RT-2 (Robotic Transformer 2) is a vision-language-action (VLA) model that co-fine-tunes a large vision-language model on both web data and robotic trajectories. By tokenizing robot actions as text tokens, RT-2 enables a single model to perform visual question answering, image captioning, and robot control simultaneously. It demonstrates emergent capabilities such as reasoning about previously unseen objects, interpreting abstract instructions, and performing multi-step manipulation tasks, all without task-specific training.
RT-2 is a web-knowledge transfer model for robotics developed by Google DeepMind in 2023 for robotics.
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| Signal | Value |
|---|---|
| Definition | RT-2 is a web-knowledge transfer model for robotics developed by Google DeepMind in 2023 for robotics. |
| Short Description | A vision-language-action model that transfers web-scale knowledge directly to robot control. |
| Benchmark Rows | 2 |
| FAQ Entries | 2 |
| Related Models | 4 |
| Related Guides | 0 |
| Related Research Topics | 1 |
| Last Updated | 2026-04-07 |
Key capabilities associated with this model.
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| Reference | Link |
|---|---|
| Brohan et al., 2023. RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. arXiv:2307.15818 | Open source |
Nearby models linked from the current editorial record.
| Model | Category | World Model Type | Index v1.1 |
|---|---|---|---|
| 3D-VLA | Foundation World Model | 3D-aware embodied world model | 47/100 |
| AMI World Model | Foundation World Model | Announced multimodal world model initiative | N/A |
| TD-MPC2 | Model-Based RL | Implicit dynamics + MPC planner | 80/100 |
Side-by-side comparisons already connected to this model.
| Comparison | Matchup | Summary |
|---|---|---|
| RT-2 vs 3D-VLA | RT-2 vs 3D-VLA | Two approaches to vision-language-action models for robotics. RT-2 leverages web-scale VLM knowledge through action tokenization, while 3D-VLA integrates explicit 3D spatial understanding for embodied reasoning. |
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| Topic | Summary |
|---|---|
| Language-Conditioned World Models | How language-conditioned world models use text prompts or natural-language actions to control simulation, planning, and embodied behavior across Pandora, 3D-VLA, RT-2, and hybrid systems. |
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RT-1 was trained only on robot data. RT-2 co-fine-tunes a large vision-language model (PaLI-X or PaLM-E) on both web and robot data, enabling emergent reasoning capabilities that RT-1 lacks.
Yes. By leveraging web-scale visual knowledge, RT-2 can reason about and manipulate objects not present in its robot training data, a key emergent capability.
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Published by world-models.io editorial board.
Lead editor Tyler D. - Technical editor, methodology and benchmark analysis.
This model page synthesizes primary papers, official model pages, benchmark evidence, and related world-models.io context into a reference resource.
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: 2026-04-07.
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