As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the environment. Briefly, it captures the possibilities and effects of the actions of an agent applied to a specific object or, more generally, a part of the environment. This paper provides a short review of the recent developments of deep robotic affordance learning (DRAL), which aims to develop data-driven methods that use the concept of affordance to aid in robotic tasks. We first classify these papers from a reinforcement learning (RL) perspective, and draw connections between RL and affordances. The technical details of each category are discussed and their limitations identified. We further summarise them and identify future challenges from the aspects of observations, actions, affordance representation, data-collection and real-world deployment. A final remark is given at the end to propose a promising future direction of the RL-based affordance definition to include the predictions of arbitrary action consequences.

翻译：作为心理学领域提出的一个重要概念，“可操作性”（affordance）被视为人类理解并与环境交互的关键能力之一。简而言之，它捕捉了智能体对特定物体（或更广义的环境组成部分）施加动作时的可能性和效果。本文对深度机械臂可操作性学习（DRAL）的最新进展进行了简要综述，旨在开发借助可操作性概念辅助机器人任务的数据驱动方法。我们首先从强化学习（RL）的视角对这些文献进行分类，并建立RL与可操作性之间的联系。随后讨论每一类别的技术细节并指出其局限性。进一步地，我们从观测、动作、可操作性表示、数据收集以及实际部署等方面进行总结，并识别未来面临的挑战。最后给出结论，提出一种有前景的未来方向：基于RL的可操作性定义应包含对任意动作后果的预测。