A Grasping CNN with Image Segmentation for Mobile Manipulating Robot This paper presents a grasping convolutional neural network with image segmentation for mobile manipulating robot. The proposed method is cascaded by a feature pyramid network FPN and a grasping network DrGNet. The FPN network combined with point cloud clustering is used to obtain the mask of the target object. Then, the grayscale map and the depth map corresponding to the target object are combined and sent to the DrGNet network for providing multiscale images. On this basis, depthwise separable convolution is used for encoding. The results of encoders are refined according to the lightweight RefineNet as well as sSE, which can achieve a better grasp detection. The proposed method is verified by the experiments on mobile manipulating robot.

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.