  Given point cloud input, the problem of 6-DoF grasp pose detection is to
identify a set of hand poses in SE(3) from which an object can be successfully
grasped. This important problem has many practical applications. Here we
propose a novel method and neural network model that enables better grasp
success rates relative to what is available in the literature. The method takes
standard point cloud data as input and works well with single-view point clouds
observed from arbitrary viewing directions.
