Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modelling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.

翻译：在服务机器人领域，研究者投入大量精力学习、理解并表征机器人任务执行中的操作动作。机器人学习与问题求解的任务极为宽泛，涵盖目标检测、活动识别、任务/运动规划、定位、知识表示与检索，以及感知/视觉与机器学习技术的交叉融合等多元课题。本文聚焦于知识表示这一核心，尤其是过去数十年间研究者如何收集、表征和复现知识以解决实际问题。根据知识表示的定义，我们探讨此类表示与近年来被广泛引入研究的实用学习模型（如机器学习、深度学习、概率建模和语义图结构）之间的关键区别。在对这些工具进行概述的同时，我们讨论机器人学习中存在的经典问题，以及如何构建和利用解决方案、技术或进展（若有）来应对这些挑战。最后，本文提出设计有效知识表示时应遵循的关键原则。