One of the main challenges in the vision-based grasping is the selection of feasible grasp regions while interacting with novel objects. Recent approaches exploit the power of the convolutional neural network (CNN) to achieve accurate grasping at the cost of high computational power and time. In this paper, we present a novel unsupervised learning based algorithm for the selection of feasible grasp regions. Unsupervised learning infers the pattern in data-set without any external labels. We apply k-means clustering on the image plane to identify the grasp regions, followed by an axis assignment method. We define a novel concept of Grasp Decide Index (GDI) to select the best grasp pose in image plane. We have conducted several experiments in clutter or isolated environment on standard objects of Amazon Robotics Challenge 2017 and Amazon Picking Challenge 2016. We compare the results with prior learning based approaches to validate the robustness and adaptive nature of our algorithm for a variety of novel objects in different domains.

中文翻译：

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领域独立无监督学习掌握新事物

基于视觉的抓取的主要挑战之一是在与新对象交互时选择可行的抓取区域。最近的方法利用卷积神经网络 (CNN) 的强大功能，以高计算能力和时间为代价实现准确抓取。在本文中，我们提出了一种新的基于无监督学习的算法，用于选择可行的抓取区域。无监督学习在没有任何外部标签的情况下推断数据集中的模式。我们在图像平面上应用 k-means 聚类来识别抓取区域，然后是轴分配方法。我们定义了一个新的抓取决策指数（GDI）概念来选择图像平面中的最佳抓取姿势。