Nowadays, robots are dominating the manufacturing, entertainment, and healthcare industries. Robot vision aims to equip robots with the capabilities to discover information, understand it, and interact with the environment, which require an agent to effectively understand object affordances and functions in complex visual domains. In this literature survey, first, “visual affordances” are focused on and current state-of-the-art approaches for solving relevant problems as well as open problems and research gaps are summarized. Then, sub-problems, such as affordance detection, categorization, segmentation, and high-level affordance reasoning, are specifically discussed. Furthermore, functional scene understanding and its prevalent descriptors used in the literature are covered. This survey also provides the necessary background to the problem, sheds light on its significance, and highlights the existing challenges for affordance and functionality learning.

中文翻译：

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视觉可及性和功能理解

如今，机器人正在主导制造、娱乐和医疗保健行业。机器人视觉旨在使机器人具备发现信息、理解信息并与环境交互的能力，这需要智能体有效地理解复杂视觉领域中的对象可供性和功能。在本文献调查中，首先，“视觉可供性”重点关注并总结了当前解决相关问题的最先进方法以及未解决的问题和研究空白。然后，具体讨论了子问题，例如示能检测、分类、分割和高级示能推理。此外，还涵盖了功能场景理解及其在文献中使用的普遍描述。这项调查还提供了问题的必要背景，