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（AIPP）对许多机器人应用至关重要，它使移动机器人能够高效收集关于初始未知环境的有用数据。此外，基于学习的方法在机器人领域日益普及，以增强各类复杂任务的适应性、通用性和鲁棒性。本综述探讨了将机器人学习应用于AIPP的研究，旨在弥合这两个研究领域之间的差距。我们首先为一般性AIPP问题提供统一的数学框架。随后，从（i）学习算法和（ii）机器人应用两个角度建立了两套互补的分类体系。我们分析了协同效应、最新趋势，并强调了基于学习的方法在AIPP框架中的优势。最后，我们讨论了关键挑战和未来有前景的研究方向，以通过学习实现更通用、更稳健的机器人数据采集系统。本综述提供了所评述论文的完整目录，包括公开可用的资源库，以促进该领域的后续研究。