Where we capture, structure, and scale real-world human experience
Over the past decade, AI has made remarkable progress in understanding and reasoning over text and pixels. Yet next-generation intelligence—spatial intelligence, embodied intelligence, physical reasoning, the ability to interact with the world—demands something fundamentally different.
It demands Experience.
Today's models still lack the right data to learn how humans perceive, move, manipulate, and interact within complex physical environments. Existing datasets help, but they are not enough: not large enough, not rich enough, and often not grounded in real human behavior. In many cases, we are still unsure what kind of data intelligence truly needs.
Human-level intelligence emerges from large-scale records of real human experience.
We use "Experience" deliberately. It encompasses the many dimensions of human experience—what humans see, hear, touch, move, and interact with as they live and act in the real world.
Human experience is not just perception. It is perception tightly coupled with action, intention, and physical consequence.
To capture it faithfully, we must start from the perspective where experience originates.
If we want AI systems that understand and operate in the physical world, we must teach them from the viewpoint that best reflects real human behavior: the egocentric perspective.
From the egocentric perspective, human experience naturally includes:
This is exactly the information an intelligent system needs to learn how to move, manipulate, and reason about the physical world.
Egocentric capture scales. This scalability unlocks diversity, realism, and long-tail behaviors that are critical for robust generalization.
Raw experience alone is not enough. Intelligence requires structure.
Our platform includes a suite of spatial foundation models that automatically transform captured experience into machine-readable annotation, including:
Together, these models convert unstructured human experience into structured interactive intelligence datasets, ready for training the next generation of intelligent systems.
Intelligence does not emerge from passive observation alone. It emerges through re-experiencing: the process by which AI systems repeatedly relive, model, and internalize human experience.
We see immediate impact of re-experiencing across three frontier directions:
Egocentric human experience provides realistic trajectories for learning how environments evolve and how actions lead to consequences. This grounds world models in real perception and interaction, improving physical prediction and reasoning. At scale, it enables world models that generalize across diverse, unstructured real-world scenarios.
Simulation remains essential for robot learning, but its fidelity is fundamentally limited by the realism of its assets. Human experience supplies natural motion patterns, contact dynamics, and task distributions that simulations often miss. By grounding simulation in real human behavior, Real2Sim produces training environments that are more representative, diverse, and effective.
Large-scale egocentric experience exposes vision-language-action models to how humans actually perceive, act, and describe tasks in real environments. This grounding leads to much stronger generalization across new tasks, instructions, and scenes. As experience scales, so does the model's ability to adapt and act intelligently in the open world.
If we want AI that moves like us, understands like us, and helps us in everyday physical tasks, then its intelligence must be built from human experience itself. We are building systems that capture, structure, and transform human experience into the foundation for machine intelligence.
Intelligence begins with experience. We're here to scale it.