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重要概念，可供性（affordance）被视为人类理解环境并与环境交互的关键能力之一。简而言之，它描述了智能体对特定物体（或更广义的环境部分）施加动作的可能性及其效果。本文对深度机器人可供性学习（DRAL）的最新进展进行了简短综述，该领域致力于开发基于数据驱动的方法，利用可供性概念辅助机器人任务。我们首先从强化学习（RL）的视角对这些论文进行分类，并建立强化学习与可供性之间的关联。随后讨论了各类别的技术细节及其局限性。进一步从观测、动作、可供性表示、数据采集及实际部署等维度进行归纳，并指出未来挑战。最后，本文提出了一种有前景的未来方向——基于强化学习的可供性定义应当包含对任意动作后果的预测能力。