Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep-learning methods have allowed rapid progress in robotic object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular interest in grasping an object using all 6 degrees of freedom of the end-effector pose. Our review found four common methodologies for robotic grasping: sampling-based approaches, direct regression, reinforcement learning, and exemplar approaches. Additionally, we found two `supporting methods` around grasping that use deep-learning to support the grasping process, shape approximation, and affordances. We have distilled the publications found in this systematic review (85 papers) into ten key takeaways we consider crucial for future robotic grasping and manipulation research. An online version of the survey is available at https://rhys-newbury.github.io/projects/6dof/

翻译：抓取是通过在一组接触点施加力和力矩来拾取物体的过程。深度学习方法的近期进展推动了机器人物体抓取技术的快速发展。在本系统性综述中，我们调查了过去十年间的相关出版物，特别关注使用末端执行器姿态的全部六个自由度来抓取物体的研究。我们的综述发现了机器人抓取的四种常见方法：基于采样的方法、直接回归方法、强化学习方法和示例方法。此外，我们还发现了两种围绕抓取的“辅助方法”，它们利用深度学习支持抓取过程，即形状近似和可供性分析。我们从本系统性综述中的85篇论文中提炼出十个关键要点，认为这些对未来机器人抓取与操作研究至关重要。该综述的在线版本可访问：https://rhys-newbury.github.io/projects/6dof/