Robots are learning to fold laundry, sort parts, and handle unfamiliar objects. But those demonstrations are often rather narrow and don't explain what a robot has learned or why it fails.
To study a complex system, scientists often begin with a small, controlled environment, such as a petri dish or terrarium, where they can vary conditions and observe the effects. Robotics needs environments that make this kind of investigation possible—researchers should be able to change the training data or model design, repeat a test, and trace the cause of a failure.
Today we're introducing a framework for creating these environments; we call them FoundationWorlds. Each pairs a controlled, simulated world targeted at studying robotics in a specific environment with tools for automatically generating tasks and training demonstrations, as well as metrics for measuring a robot's behavior over time. Together, they provide a setting for studying questions such as how models improve as more training data becomes available, and how to best combine high-level reasoning with precise physical control.
