Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective
Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective
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DOI：
10.1109/tcds.2023.3277288
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发表时间：
2023-03
影响因子：
5
通讯作者：
Xintong Yang;Ze Ji;Jing Wu;Yunyu Lai
中科院分区：
文献类型：
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作者：
Xintong Yang;Ze Ji;Jing Wu;Yunyu Lai
作为心理学领域提出的一个流行概念，可视性被认为是人类理解环境和与环境互动的重要能力之一。简而言之，它捕获应用于特定对象或更一般地说，环境的一部分的代理动作的可能性和效果。本文简要回顾了深度机器人功能学习（DRAL）的最新发展，其目的是开发数据驱动的方法，使用功能概念来帮助机器人完成任务。我们首先从强化学习（RL）的角度对这些论文进行分类，并在RL和启示之间建立联系。讨论了每个类别的技术细节，并确定了它们的局限性。我们进一步总结了它们，并从观察、行动、提供性表示、数据收集和实际部署等方面确定了未来的挑战。最后给出了最后的评论，提出了基于强化学习的可视性定义的一个有希望的未来方向，包括对任意行为后果的预测。
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 article 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 are identified. We further summarize 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.