We build the generative infrastructure required to scale robotic intelligence, moving past the limitations of manual data collection to unlock truly generalist machines.
We leverage latest-frontier video foundation models to synthesize millions of hours of video-action training data. By generating high-fidelity, physically consistent video data, we bridge the gap between limited real-world demonstrations and the scale required for general intelligence.
Trained on a mixture of real-world interactions and large-scale generative datasets, our models learn the underlying laws of physics through observation and prediction, enabling zero-shot generalization across diverse hardware and tasks.
Achieving general-purpose robotics requires a fundamental shift in how we think about data. We are building the engine for that shift, merging generative AI with physical embodiment to create a future where robots can learn as fast as they can see.