Gathering real-world data from the robot quickly becomes a bottleneck when constructing a robot learning system for grasping. In this work, we design a semi-supervised grasping system that, on top of a small sample of robot experience, takes advantage of images of products to be picked, which are collected without any interactions with the robot. We validate our findings both in the simulation and in the real world. In the regime of a small number of robot training samples, taking advantage of the unlabeled data allows us to achieve performance at the level of 10-fold bigger dataset size used by the baseline. The code and datasets used in the paper will be released at https://github.com/nomagiclab/grasping-student.

翻译：[translated abstract in Chinese] 从真实机器人中收集实际数据在构建用于抓取的机器人学习系统时迅速成为瓶颈。本研究设计了一种半监督抓取系统，该系统在少量机器人经验样本的基础上，利用无需与机器人交互即可收集的待抓取产品图像。我们通过仿真和真实世界环境验证了研究成果。在小规模机器人训练样本条件下，利用无标注数据使我们能够达到基线方法使用十倍数据集规模时的性能水平。本文使用的代码与数据集将发布于 https://github.com/nomagiclab/grasping-student。