Attention Grasping Network: A Realtime Approach to Generating Grasp Synthesis This paper presents a realtime, pixelwise method to generate grasp synthesis based on fully convolutional netural networks (FCN). Our proposed Attention Grasping Network (AGN) applies a novel attention mechanism to robotic grasp detection, which automatically learns to focus on salient features of the input image. The model with attention mechnisms can compensate for the loss of detail information in standard FCN, which increases the sensitivity of the model and accuracy of prediction. In addition,in order to ensure a realtime grasp and save computing resources, the lightweight AGN model predicts the position and angle of grasping point. Our method only takes 22ms to execute the grasp detection pipeline on a GPUequipped computer, and achieves 97.8% accuracy on Cornell Grasping Dataset.

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.