A robotics foundation model learns reusable representations and behaviors from varied robot, visual, language, or action data. Applications can adapt that broad capability to particular tasks through prompting, demonstrations, fine-tuning, or additional control layers.
General capability does not guarantee safe performance on a new machine or environment. Physical deployment requires task evaluation, motion constraints, failure recovery, and evidence that the model handles unfamiliar objects and conditions reliably.
