Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations

Understand

This paper focuses on the problem of learning 6-DOF grasping with a parallel jaw gripper in simulation.

• We propose the notion of a geometry-aware representation in grasping based on the assumption that knowledge of 3D geometry is at the heart of interaction.
• Our key idea is constraining and regularizing grasping interaction learning through 3D geometry prediction.
• Specifically, we formulate the learning of deep geometry-aware grasping model in two steps: First, we learn to build mental geometry-aware representation by reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via generative 3D shape modeling.