Meta-Learning

Learning to learn — training a model on a distribution of tasks so that it can rapidly adapt to new tasks with minimal data. In robot learning, meta-learning approaches like MAML, ProMP, and task-conditioned policies enable few-shot adaptation to new objects, environments, or task variations. The model learns an initialization or adaptation strategy that is broadly effective across the task distribution.

See this in practice: the Robotics Academy →