Physical World Data Layer

- General-purpose robotics and embodied AI systems require massive amounts of high-quality real-world interaction data: how people move, manipulate objects, navigate spaces, complete tasks, and respond to changing environments. - Collecting that data is expensive, slow, and difficult to scale. Robot fleets are limited, physical labs are costly, and custom sensor setups make data collection hard to repeat across diverse environments. - At the same time, smartphones, wearable sensors, egocentric cameras, and head-mounted devices already capture rich physical-world signals at global scale, but there is no simple infrastructure layer for turning that distributed capture into reliable robotics-ready data.

- Lower the cost and friction of collecting high-quality physical-world data. - Coordinate distributed data capture with clear tasks, quality checks, and consent controls. - Create training-ready datasets for robotics, embodied AI, or real-world agent behavior.

Real-world interaction data is collected through robot fleets, labs and custom sensors, which is expensive and slow.