Generalization (robot policy)

Generalization measures how well a robot policy performs on objects, scenes, or tasks it has not seen during training. It is the central challenge of robot learning: a policy that memorizes training demonstrations but fails on novel instances has no practical value. Researchers distinguish object generalization (new instances of known categories), category generalization (entirely new object classes), and task generalization (new instruction phrasings or goal configurations). Improving generalization typically requires larger and more diverse training data, co-training with internet data, domain randomization in simulation, and foundation model priors.