Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard coding efficient and safe trajectories is challenging and time consuming. Machine learning methods have the potential to learn such controllers based on expert demonstrations. Despite promising advances, better approaches must be developed to improve safety, reliability, and efficiency of ML methods in both training and deployment phases. This survey aims to review cutting edge technologies and recent trends on ML methods applied to real-world manipulation tasks. After reviewing the related background on ML, the rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue. The paper is closed with important research directions for future works.

翻译：自动化工业带来了高质量生产、更低的制造成本以及人力资源的更好利用。机器人操作臂在自动化过程中扮演着重要角色。然而，对于复杂的操作任务，硬编码高效且安全的轨迹既具挑战性又耗时。机器学习方法有潜力基于专家示范来学习此类控制器。尽管已有令人瞩目的进展，但仍需开发更好的方法以提升机器学习方法在训练和部署阶段的安全性、可靠性与效率。本综述旨在评述应用于实际操作任务的前沿技术及最新趋势。在回顾机器学习的相关背景后，本文剩余部分致力于探讨机器学习在工业、医疗、农业、航天、军事及搜救等不同领域的应用。最后，本文以未来研究的重要方向作为收尾。