Learning Based Robot Grasping currently involves the use of labeled data. This approach has two major disadvantages. Firstly, labeling data for grasp points and angles is a strenuous process, so the dataset remains limited. Secondly, human labeling is prone to bias due to semantics. In order to solve these problems we propose a simpler self-supervised robotic setup, that will train a Convolutional Neural Network (CNN). The robot will label and collect the data during the training process. The idea is to make a robot that is less costly, small and easily maintainable in a lab setup. The robot will be trained on a large data set for several hundred hours and then the trained Neural Network can be mapped onto a larger grasping robot.

翻译：当前基于学习的机器人抓取方法通常依赖于标注数据。这种途径存在两大主要缺陷：首先，为抓取点与角度标注数据的过程极为耗时费力，导致数据集规模受限；其次，人工标注易受语义偏见影响。为解决这些问题，我们提出一种更简化的自监督机器人系统，用于训练卷积神经网络（CNN）。该机器人将在训练过程中自主完成数据标注与采集。我们的核心构想是构建一种成本更低、体积更小且易于实验室环境维护的机器人系统。该机器人将在数百小时的大规模数据集上进行训练，随后训练完成的神经网络可迁移至更大型的抓取机器人平台。