  Adaptive informative path planning (AIPP) is important to many robotics
applications, enabling mobile robots to efficiently collect useful data about
initially unknown environments. In addition, learning-based methods are
increasingly used in robotics to enhance adaptability, versatility, and
robustness across diverse and complex tasks. Our survey explores research on
applying robotic learning to AIPP, bridging the gap between these two research
fields. We begin by providing a unified mathematical framework for general AIPP
problems. Next, we establish two complementary taxonomies of current work from
the perspectives of (i) learning algorithms and (ii) robotic applications. We
explore synergies, recent trends, and highlight the benefits of learning-based
methods in AIPP frameworks. Finally, we discuss key challenges and promising
future directions to enable more generally applicable and robust robotic
data-gathering systems through learning. We provide a comprehensive catalogue
of papers reviewed in our survey, including publicly available repositories, to
facilitate future studies in the field.
