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.

翻译：自适应信息路径规划对于许多机器人应用至关重要，它能使移动机器人在未知环境中高效收集有用数据。此外，基于学习的方法在机器人领域日益广泛地被采用，以增强机器人在多样化复杂任务中的适应性、多功能性和鲁棒性。本综述探讨了将机器人学习应用于自适应信息路径规划的研究，旨在弥合这两个研究领域之间的鸿沟。我们首先为一般的自适应信息路径规划问题建立统一的数学框架。随后，我们从（i）学习算法和（ii）机器人应用两个视角，构建了当前工作的两个互补性分类体系。我们探索协同效应、最新趋势，并强调基于学习的方法在自适应信息路径规划框架中的优势。最后，我们讨论关键挑战和有前景的未来方向，以通过学习方法实现更具通用性和鲁棒性的机器人数据采集系统。本综述提供了所评述论文的全面目录（包括公开可用的资源库），以促进该领域后续研究的发展。