利用大规模视觉模型增强机器人抓取的简要综述
A Brief Survey on Leveraging Large Scale Vision Models for Enhanced Robot Grasping
AI总结：
本文综述大规模视觉预训练模型如何缓解机器人抓取中的数据稀缺问题，分析关键挑战并指出未来研究方向。
摘要：
机器人抓取在现实场景中是一项困难的运动任务，构成了在各行业部署高性能机器人的主要障碍。值得注意的是，数据稀缺使得抓取对于学习型模型尤其具有挑战性。近期计算机视觉的进展见证了基于互联网海量数据的成功无监督训练机制的发展，如今几乎所有主流模型都采用预训练骨干网络。在此背景下，我们开始研究大规模视觉预训练在提升机器人抓取性能方面的潜在收益。这一初步文献综述阐明了关键挑战，并描绘了面向机器人操作的视觉预训练未来研究的可能方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
