  Grasp synthesis is one of the challenging tasks for any robot object
manipulation task. In this paper, we present a new deep learning-based grasp
synthesis approach for 3D objects. In particular, we propose an end-to-end 3D
Convolutional Neural Network to predict the objects' graspable areas. We named
our approach Res-U-Net since the architecture of the network is designed based
on U-Net structure and residual network-styled blocks. It devised to plan 6-DOF
grasps for any desired object, be efficient to compute and use, and be robust
against varying point cloud density and Gaussian noise. We have performed
extensive experiments to assess the performance of the proposed approach
concerning graspable part detection, grasp success rate, and robustness to
varying point cloud density and Gaussian noise. Experiments validate the
promising performance of the proposed architecture in all aspects. A video
showing the performance of our approach in the simulation environment can be
found at: http://youtu.be/5_yAJCc8owo
