  Proposing grasp poses for novel objects is an essential component for any
robot manipulation task. Planning six degrees of freedom (DoF) grasps with a
single camera, however, is challenging due to the complex object shape,
incomplete object information, and sensor noise. In this paper, we present a
6-DoF contrastive grasp proposal network (CGPN) to infer 6-DoF grasps from a
single-view depth image. First, an image encoder is used to extract the feature
map from the input depth image, after which 3-DoF grasp regions are proposed
from the feature map with a rotated region proposal network. Feature vectors
that within the proposed grasp regions are then extracted and refined to 6-DoF
grasps. The proposed model is trained offline with synthetic grasp data. To
improve the robustness in reality and bridge the simulation-to-real gap, we
further introduce a contrastive learning module and variant image processing
techniques during the training. CGPN can locate collision-free grasps of an
object using a single-view depth image within 0.5 seconds. Experiments on a
physical robot further demonstrate the effectiveness of the algorithm.
