  Inherent morphological characteristics in objects may offer a wide range of
plausible grasping orientations that obfuscates the visual learning of robotic
grasping. Existing grasp generation approaches are cursed to construct
discontinuous grasp maps by aggregating annotations for drastically different
orientations per grasping point. Moreover, current methods generate grasp
candidates across a single direction in the robot's viewpoint, ignoring its
feasibility constraints. In this paper, we propose a novel augmented grasp map
representation, suitable for pixel-wise synthesis, that locally disentangles
grasping orientations by partitioning the angle space into multiple bins.
Furthermore, we introduce the ORientation AtteNtive Grasp synthEsis (ORANGE)
framework, that jointly addresses classification into orientation bins and
angle-value regression. The bin-wise orientation maps further serve as an
attention mechanism for areas with higher graspability, i.e. probability of
being an actual grasp point. We report new state-of-the-art 94.71% performance
on Jacquard, with a simple U-Net using only depth images, outperforming even
multi-modal approaches. Subsequent qualitative results with a real bi-manual
robot validate ORANGE's effectiveness in generating grasps for multiple
orientations, hence allowing planning grasps that are feasible.
