Introducing Oasis 3: First Interactive World Model for Physical AI

Robots are about to be everywhere: driving our roads, working in our warehouses, helping in our homes. The bottleneck isn't ambition or hardware. It's training: a robot has to experience the world, in all its complexity and danger, before it can be trusted to act in it.

Today we're launching Oasis 3, a generative world model built for physical AI, starting with autonomous vehicles. It generates infinitely diverse, hyper-realistic, interactive environments from a single prompt — and it's the first world model accessible by API, live from day one. If you're training robots, you can start using it today.

Explore Oasis 3

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Why Do World Models Matter?

Robots, like people, learn by example and by experience — trial, error, and feedback. Watching demonstrations is not enough; a robot has to act, fail, and try again. In other words, robot learning needs to be closed-loop: the robot acts, the world responds, and the robot learns from what happens next.

The real world is a terrible place to do that. It's slow, expensive, and unsafe — and it almost never serves up the situations that matter most. The hardest and most important part of training a robot is exposure to rare, long-tail events: the child chasing a ball into a snowy street, the truck running a red light at dusk. You can't schedule those in the real world, and you wouldn't want to.

So the industry turned to simulation. Classic simulators were a great first step, but they have three structural problems:

1. Scale: every environment takes hundreds of expert hours to hand-build, so coverage of the real world's diversity is permanently out of reach.
2. Visual Sim2Real gap: their graphics look closer to video games than to reality, so what a robot sees in simulation doesn't match what its cameras will see on the road.
3. Physical Sim2Real gap: the physical accuracy of simulators falls short of realism, making it hard for robots trained in simulators to be deployed in real-world settings.

What robots actually need is something else: environments that are realistic enough to transfer to the real world, interactive enough to learn from in a closed loop, diverse enough to cover the long tail, and cheap enough to run for millions of hours. That is what a world model is supposed to be.

Oasis 3 output: “An overcast morning illuminates a suburban road under diffuse lighting.” A synchronized 3-camera driving view, creating one continuous road environment across all angles.

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What Oasis 3 Is

Oasis 3 is a generative world model: text prompt in, a living world out. A robot acts inside an infinite world in real time and receives realistic observations and feedback in return. The environment is not generated once and replayed — it continues to evolve in response to the robot's actions. Concretely:

• Promptable. Any environment from a single prompt. A snowy nighttime scene with the Eiffel Tower in the background used to mean weeks of expert effort in a classic simulator; in Oasis 3 it's a sentence. The same goes for the scenarios that matter most and occur the least: whiteouts and blizzards, mountain roads and tunnels, a camera obstructed by mud, steam, or debris.
• Hyper-realistic. Built on generative AI rather than hand-authored assets, Oasis 3 produces visuals at real-life quality, not video-game quality — closing the gap that keeps classically simulated training data from transferring to the real world.
• Infinite and interactive. Oasis 3 generates closed-loop interactions of unbounded length and complexity, in real time. Other world models cap out after a few minutes or fall back on less general approaches that can't capture the full complexity of the real world.
• Robot-native. Most world models take keyboard-and-mouse input — they're built for people. Oasis 3 is action-conditioned on robot actions and produces synchronized multi-camera observations as output, so a robot sees all around itself, the way it does in the real world.
• API-accessible. Until now, world models have been research demos or closed internal tools. Oasis 3 is the first you can simply call: API access from day one, ready to integrate into real robotics simulation pipelines.

Most world models pick a tradeoff: generality, length, or efficiency. Oasis 3 doesn't make you choose.

[video of collage of different identifiable places]

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How We Made It Happen

Live Stream Diffusion. Oasis 3 generates worlds autoregressively — frame by frame, with each new frame conditioned on the frames before it and on the robot's latest action. That action-conditioning is what closes the loop. Diffusion is a general solution that scales with data and isn't explicitly bounded the way representations like Gaussian splats are — which is what lets Oasis 3 generate unbounded, fully general worlds rather than replaying a fixed scene. The same real-time foundation behind Oasis 1 and Oasis 2 keeps generation coherent across long, unbounded sessions — correcting drift as it goes rather than letting it accumulate.

Multi-view consistency. Robots don't see the world through one camera. A geometry-aware architecture keeps Oasis 3's three synchronized camera views spatially consistent with each other, so the output reads as one continuous world rather than three independent videos — and the robot's full surround perception can be trained and evaluated faithfully.

Realistic data and post-training. Hyper-realism doesn't come for free. Oasis 3 is trained on real-world data and refined through dedicated post-training for realism, which is what separates its output from the video-game look of classic simulation.

DOS — the Decart Optimization Stack. World models will be used to simulate millions of hours, so usability lives or dies on efficiency. DOS delivers real-time, immediately interactive generation 100x more efficiently. Today, Oasis 3 runs at 22 FPS at 512×768×3, with under 200 ms of latency.

The system runs on CoreWeave’s purpose-built AI cloud and is built on top of NVIDIA’s physical AI ecosystem, supporting the performance needed for real-time simulation workflows.

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What You Can Do With It

Oasis 3 supports every step of the robot lifecycle (learning, practicing, testing) and unlocks a new class of physical AI workflows:

• Online reinforcement learning: use RL to train inside a generative world model: policies act, get feedback, and improve in a closed loop.
• Policy evaluation & validation: test against the exact scenarios that matter before deployment.
• High-fidelity teleoperation: Let human operators control robotic systems to create examples inside of Oasis 3 to create human expert training examples.

We're already working closely with industry leaders to bring general-purpose robots one step closer to reality.

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The Era Ahead

Oasis began as an interactive world model for gaming. With Oasis 1 and Oasis 2, we introduced real-time generation and interaction with virtual worlds; with Oasis 3, that same foundation now powers physical AI.

We're starting with autonomous vehicles and will generalize to other embodiments and industries — humanoids and manufacturing robots tackling dexterous manipulation, off-road and broader mobility systems where data is scarce. Our vision: every robot, no matter how simple or complex, trained and evaluated inside the Oasis world model — safely interacting with the full complexity of the real world inside a virtual one, at a scale the real world could never allow.

Every robot will be trained inside a world model. That era starts today.

Read the docs and get API access

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About Decart

Decart is a fully vertically integrated frontier AI research lab, founded in 2023 and building state-of-the-art, real-time world models and the ultra-optimized infrastructure to power them. At the core of the company is the Decart Optimization Stack (DOS), spanning hardware-aware model design, data pipelines, inference optimization, and productization, enabling real-time video generation at a fraction of industry cost. Backed by Radical Ventures, NVIDIA, Sequoia Capital, Benchmark, Zeev Ventures and many of the industry’s leading strategic and angel investors, Decart has raised over $450 million to date.

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