Dexterous manipulation, especially dexterous grasping, is a primitive and crucial ability of robots that allows the implementation of performing human-like behaviors. Deploying the ability on robots enables them to assist and substitute human to accomplish more complex tasks in daily life and industrial production. A comprehensive review of the methods based on point cloud and deep learning for robotics dexterous grasping from three perspectives is given in this paper. As a new category schemes of the mainstream methods, the proposed generation-evaluation framework is the core concept of the classification. The other two classifications based on learning modes and applications are also briefly described afterwards. This review aims to afford a guideline for robotics dexterous grasping researchers and developers.

中文翻译：

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机器人灵巧抓取：基于点云和深度学习的方法

灵巧操纵，尤其是灵巧抓取，是机器人一项原始而关键的能力，它可以实现类似人类的行为。将这种能力部署在机器人身上，使其能够辅助和替代人类完成日常生活和工业生产中更复杂的任务。本文从三个角度对基于点云和深度学习的机器人灵巧抓取方法进行了全面综述。作为主流方法的新类别方案，所提出的生成评估框架是分类的核心概念。另外两种基于学习模式和应用的分类也将在后面简要介绍。本综述旨在为机器人灵巧抓取研究人员和开发人员提供指导。