Robot Learning

Few-Shot Imitation

Few-shot imitation is the ability of a robot to acquire a new task from only a handful of demonstrations, often just one, rather than the hundreds typically needed for behavior cloning. Approaches include meta-learning across task distributions, as in one-shot imitation learning by Duan et al. (2017), retrieval-augmented policies, and prompting large pretrained robot models with demonstrations as context.

Why it matters for physical AI

Demonstration collection is the dominant cost in scaling robot skills; models that adapt from a few examples make deploying new tasks on-site economically viable rather than a data-engineering project.