DomainIndependent Unsupervised Detection of Grasp Regions to grasp Novel Objects One of the main challenges in the visionbased grasping is the selection of feasible grasp regions while interacting with novel objects. Recent approaches exploit the power of 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 dataset without any external labels. We applied kmeans clustering at every sampling stage in image plane to identify the grasp regions, followed by axes assignment method. We define a novel concept of Grasp Decide Index (GDI) to select the best grasp pose in the 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 compared 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.

ROIbased Robotic Grasp Detection for Object Overlapping Scenes Grasp detection considering the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROIGD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROIGD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the secondstage is the grasp detector based on ROI features. We also contribute a multiobject grasp dataset, (a) which is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation Relationship Dataset. Experimental results demonstrate that ROIGD performs much better in object overlapping scenes and at the meantime, remains comparable with stateoftheart grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROIGD can help robots grasp the target in singleobject and multiobject scenes with the overall success rates of 92.5% and 83.8% respectively.