  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/
