  Robotic grasping presents a difficult motor task in real-world scenarios,
constituting a major hurdle to the deployment of capable robots across various
industries. Notably, the scarcity of data makes grasping particularly
challenging for learned models. Recent advancements in computer vision have
witnessed a growth of successful unsupervised training mechanisms predicated on
massive amounts of data sourced from the Internet, and now nearly all prominent
models leverage pretrained backbone networks. Against this backdrop, we begin
to investigate the potential benefits of large-scale visual pretraining in
enhancing robot grasping performance. This preliminary literature review sheds
light on critical challenges and delineates prospective directions for future
research in visual pretraining for robotic manipulation.
