论文标题
独立的无监督学习以掌握新物体
Domain Independent Unsupervised Learning to grasp the Novel Objects
论文作者
论文摘要
基于视觉的抓地力的主要挑战之一是在与新物体互动时选择可行的掌握区域。最近的方法利用了卷积神经网络（CNN）的力量，以高计算能力和时间的成本来实现准确的掌握。在本文中，我们提出了一种基于无监督的学习算法，用于选择可行的掌握区域。无监督的学习侵入数据集中的模式，而无需任何外部标签。我们在图像平面上应用K-均值聚类以识别GRASP区域，然后使用轴分配方法。我们定义了一个新颖的掌握决定索引（GDI）的概念，以选择图像平面中的最佳掌握姿势。我们已经在杂物或孤立的环境中进行了几项实验，对2017年亚马逊机器人技术挑战的标准对象和2016年亚马逊采摘挑战挑战。我们将结果与先前的基于学习的方法进行了比较，以验证算法的鲁棒性和适应性性质，以了解不同领域中各种新颖对象的算法。
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.
