Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications. Yet, although their potential has been widely recognised in the machine learning community, graph learning is largely unexplored for downstream tasks such as robotics applications. To fully unlock their potential, hence, we propose a review of graph neural architectures from a robotics perspective. The paper covers the fundamentals of graph-based models, including their architecture, training procedures, and applications. It also discusses recent advancements and challenges that arise in applied settings, related for example to the integration of perception, decision-making, and control. Finally, the paper provides an extensive review of various robotic applications that benefit from learning on graph structures, such as bodies and contacts modelling, robotic manipulation, action recognition, fleet motion planning, and many more. This survey aims to provide readers with a thorough understanding of the capabilities and limitations of graph neural architectures in robotics, and to highlight potential avenues for future research.

翻译：面向图的深度神经网络已成为处理复杂非欧几里得数据的有力工具，此类数据在各类应用场景中日渐普遍。尽管其潜力已在机器学习领域得到广泛认可，但在机器人应用等下游任务中，图学习仍有待深入探索。为充分释放其潜力，本文从机器人学视角对图神经架构进行了综述。文章涵盖图模型的基础知识，包括其架构、训练流程及应用，并讨论了实际应用中的最新进展与挑战，例如感知、决策与控制的集成。最后，本文广泛综述了多种受益于图结构学习的机器人应用，包括身体与接触建模、机器人操作、动作识别、编队运动规划等。本综述旨在帮助读者全面理解图神经架构在机器人学中的能力与局限，并指出未来研究的潜在方向。