The robotic grasping task persists as a modern industry problem that seeks autonomous, fast implementation, and efficient techniques. Domestic robots are also a reality demanding a delicate and accurate human–machine interaction, with precise robotic grasping and handling. From decades ago, with analytical heuristics, to recent days, with the new deep learning policies, grasping in complex scenarios is still the aim of several works’ that propose distinctive approaches. In this context, this paper aims to cover recent methodologies’ development and discuss them, showing state-of-the-art challenges and the gap to industrial applications deployment. Given the complexity of the related issue associated with the elaborated proposed methods, this paper formulates some fair and transparent definitions for results’ assessment to provide researchers with a clear and standardised idea of the comparison between the new proposals.

机器人的抓取任务作为一种现代工业问题而持续存在，以寻求自主，快速实施和高效的技术。家用机器人也是一个现实，需要精确，精确的人机交互以及精确的机器人抓握和处理。从几十年前开始，随着分析启发式技术的发展，直到最近，随着新的深度学习策略的出现，在复杂场景中进行抓取仍然是几本提出独特方法的著作的目标。在这种情况下，本文旨在介绍最新方法的发展并进行讨论，以显示最新的挑战以及与工业应用程序部署之间的差距。考虑到与拟议的拟议方法相关的相关问题的复杂性，