Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation

Understand

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.