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One of the main challenges in the vision-based grasping is the selection offeasible grasp regions while interacting with novel objects. Recent approachesexploit the power of the convolutional neural network (CNN) to achieve accurategrasping at the cost of high computational power and time. In this paper, wepresent a novel unsupervised learning based algorithm for the selection offeasible grasp regions. Unsupervised learning infers the pattern in data-setwithout any external labels. We apply k-means clustering on the image plane toidentify the grasp regions, followed by an axis assignment method. We define anovel concept of Grasp Decide Index (GDI) to select the best grasp pose inimage plane. We have conducted several experiments in clutter or isolatedenvironment on standard objects of Amazon Robotics Challenge 2017 and AmazonPicking Challenge 2016. We compare the results with prior learning basedapproaches to validate the robustness and adaptive nature of our algorithm fora variety of novel objects in different domains.

Domain Independent Unsupervised Learning to grasp the Novel Objects