Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain.

近年来，强化学习在机器人技术中用于接触丰富的操作任务的研究和应用呈爆炸式增长。它处理非结构化环境和完成难以设计的行为的能力使强化学习代理越来越多地应用于现实生活场景。然而，强化学习要成为工业应用的核心要素，还有很长的路要走。本文审视了强化学习的概况，并回顾了 2017 年至今其在接触丰富的任务中的应用进展。该分析调查了在刚性和可变形物体操作中测试强化学习算法的最常用任务的主要研究。此外，探讨了与串行机械手相关的强化学习的趋势，以及这种机器学习控制技术目前提出的各种技术挑战。最后，基于最先进的技术和研究之间的共性，提出了一个与强化学习在接触丰富的操作任务中的主要概念相关的框架。这篇综述的最终目标是支持机器人社区在强化学习系统的未来发展中，讨论这项技术的主要挑战，并提出该领域未来的研究方向。提出了一个与强化学习在接触丰富的操作任务中的主要概念相关的框架。这篇综述的最终目标是支持机器人社区在强化学习系统的未来发展中，讨论这项技术的主要挑战，并提出该领域未来的研究方向。提出了一个与强化学习在接触丰富的操作任务中的主要概念相关的框架。这篇综述的最终目标是支持机器人社区在强化学习系统的未来发展中，讨论这项技术的主要挑战，并提出该领域未来的研究方向。