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.

翻译：机器人抓取在现实场景中是一项复杂的运动任务，构成了各行业部署高性能机器人的主要障碍。值得注意的是，数据稀缺性使得学习模型在抓取任务中面临特殊挑战。近期计算机视觉领域的进展表明，基于互联网海量数据的无监督训练机制已取得显著成功，目前几乎所有主流模型都采用预训练骨干网络。在此背景下，我们开始探究大规模视觉预训练对提升机器人抓取性能的潜在优势。本初步文献综述揭示了关键挑战，并勾勒出机器人操作视觉预训练领域的未来研究方向。