Fast Convolutional Neural Network for RealTime Robotic Grasp Detection The development of the robotics field has not yet allowed robots to execute, with dexterity, simple actions performed by humans. One of them is the grasping of objects by robotic manipulators. Aiming to explore the use of deep learning algorithms, specifically Convolutional Neural Networks (CNN), to approach the robotic grasping problem, this work addresses the visual perception phase involved in the task. To achieve this goal, the dataset xe2x80x9cCornell Graspxe2x80x9d was used to train a CNN capable of predicting the most suitable place to grasp the object. It does this by obtaining a grasping rectangle that symbolizes the position, orientation, and opening of the robotu0027s parallel grippers just before the grippers are closed. The proposed system works in realtime due to the small number of network parameters. This is possible by means of the data augmentation strategy used. The efficiency of the detection is in accordance with the state of the art and the speed of prediction, to the best of our knowledge, is the highest in the literature.

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.