  Active learning is a decision-making process. In both abstract and physical
settings, active learning demands both analysis and action. This is a review of
active learning in robotics, focusing on methods amenable to the demands of
embodied learning systems. Robots must be able to learn efficiently and
flexibly through continuous online deployment. This poses a distinct set of
control-oriented challenges -- one must choose suitable measures as objectives,
synthesize real-time control, and produce analyses that guarantee performance
and safety with limited knowledge of the environment or robot itself. In this
work, we survey the fundamental components of robotic active learning systems.
We discuss classes of learning tasks that robots typically encounter, measures
with which they gauge the information content of observations, and algorithms
for generating action plans. Moreover, we provide a variety of examples -- from
environmental mapping to nonparametric shape estimation -- that highlight the
qualitative differences between learning tasks, information measures, and
control techniques. We conclude with a discussion of control-oriented open
challenges, including safety-constrained learning and distributed learning.
