  Humans excel in grasping and manipulating objects because of their life-long
experience and knowledge about the 3D shape and weight distribution of objects.
However, the lack of such intuition in robots makes robotic grasping an
exceptionally challenging task. There are often several equally viable options
of grasping an object. However, this ambiguity is not modeled in conventional
systems that estimate a single, optimal grasp position. We propose to tackle
this problem by simultaneously estimating multiple grasp poses from a single
RGB image of the target object. Further, we reformulate the problem of robotic
grasping by replacing conventional grasp rectangles with grasp belief maps,
which hold more precise location information than a rectangle and account for
the uncertainty inherent to the task. We augment a fully convolutional neural
network with a multiple hypothesis prediction model that predicts a set of
grasp hypotheses in under 60ms, which is critical for real-time robotic
applications. The grasp detection accuracy reaches over 90% for unseen objects,
outperforming the current state of the art on this task.
