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:作为在心理学领域提出的一种普遍概念, " 宽限 " 被视为使人类能够理解和与环境互动的重要能力之一。简而言之,它抓住了用于特定物体或更一般而言环境一部分的代理人的行动的可能性和影响。本文件简要回顾了深层机器人宽限学习(DRAL)的最新发展情况,其目的是开发数据驱动方法,利用 " 宽限 " 概念协助机器人任务。我们首先从强化学习的角度对这些文件进行分类,并在 " 宽限 " 和 " 宽限 " 之间进行联系。讨论了每一类别的技术细节并确定了其局限性。我们进一步总结了这些细节,并从观察、行动、 " 宽限 " 、 " 数据收集 " 和 " 现实世界 " 部署等方面确定了未来的挑战。最后,将提出 " 宽限 " 宽限 " 定义有希望的未来方向,以包括任意行动后果的预测。</s>