  Data-efficient learning algorithms are essential in many practical
applications where data collection is expensive, e.g., in robotics due to the
wear and tear. To address this problem, meta-learning algorithms use prior
experience about tasks to learn new, related tasks efficiently. Typically, a
set of training tasks is assumed given or randomly chosen. However, this
setting does not take into account the sequential nature that naturally arises
when training a model from scratch in real-life: how do we collect a set of
training tasks in a data-efficient manner? In this work, we introduce task
selection based on prior experience into a meta-learning algorithm by
conceptualizing the learner and the active meta-learning setting using a
probabilistic latent variable model. We provide empirical evidence that our
approach improves data-efficiency when compared to strong baselines on
simulated robotic experiments.
