Zero-shot Generalization

Zero-shot generalization is the ability of a trained policy to successfully perform on tasks, objects, or environments it has never explicitly seen during training, without any additional fine-tuning or demonstrations. True zero-shot transfer is a major goal of robot foundation models — a policy that generalizes zero-shot to novel household objects or new language instructions would dramatically reduce the data collection burden. Current VLA models show promising zero-shot language generalization (understanding novel phrasings of known task types) but still struggle with truly novel object categories or completely new manipulation skills. Improving zero-shot performance is the central motivation for scaling robot datasets and model sizes. See also Zero-shot Transfer article.