Robot Learning

Zero-Shot Generalization

Zero-shot generalization is a model's ability to perform correctly on tasks, objects, instructions, or scenes never encountered during training, without any additional data or fine-tuning. In robot learning it is measured by evaluations on held-out object categories, novel spatial arrangements, unseen language commands, and new backgrounds, with vision-language-action models inheriting much of their zero-shot semantic breadth from web-pretrained backbones.

Why it matters for physical AI

Homes and workplaces are open worlds that no dataset can enumerate, so zero-shot performance on the long tail, rather than in-distribution success rate, is the metric that decides whether robots are actually deployable.