A Brief Survey on Leveraging Large Scale Vision Models for Enhanced Robot Grasping

TL;DR

This survey explores how large-scale vision models and pretrained backbones can improve robotic grasping, highlighting current challenges and future research directions in leveraging visual pretraining for robotic manipulation.

Contribution

It provides a comprehensive overview of the potential of large-scale visual pretraining to enhance robot grasping performance and outlines key challenges and future research directions.

Findings

Large-scale visual pretraining can improve grasping accuracy.

Pretrained backbones are widely used in robotic manipulation.

Future research should address data scarcity and transfer learning challenges.

Abstract

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.