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.

Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 xc3x97 400 high resolution RGBD images. It first downsample the images to get features and then upsample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixelwise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for imagewise and 91.02% for objectwise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.