Robot Learning

Meta-Learning

Meta-learning, or learning to learn, is the training of models that can adapt rapidly to new tasks from small amounts of data by exploiting structure shared across a distribution of training tasks. Approaches include optimization-based methods such as MAML, which finds initializations amenable to fast fine-tuning, metric-based few-shot learners, and in-context adaptation, where a sequence model conditions on examples without weight updates.

Why it matters for physical AI

Robots face endless task variations that cannot all be pretrained; fast adaptation from a handful of demonstrations or trials is the capability that separates deployable generalists from fixed-repertoire systems.