Beyond Top-Grasps Through Scene Completion

TL;DR

This paper introduces a novel end-to-end method for six-degree-of-freedom grasp planning from a single RGB-D view by estimating object shape and simulating multiple viewpoints, improving grasp success rates.

Contribution

It presents a new approach that combines scene completion and viewpoint simulation to enable full 6-DOF grasp planning from a single RGB-D image.

Findings

Significant improvement in grasp success rate using simulated viewpoints.

Validated on a Franka Emika Panda robot with 429 grasps.

Simulated images outperform real camera images, especially at angled viewpoints.

Abstract

Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps. In this work, we present a method that allows end-to-end top-grasp planning methods to generate full six-degree-of-freedom grasps using a single RGB-D view as input. This is achieved by estimating the complete shape of the object to be grasped, then simulating different viewpoints of the object, passing the simulated viewpoints to an end-to-end grasp generation method, and finally executing the overall best grasp. The method was experimentally validated on a Franka Emika Panda by comparing 429 grasps generated by the state-of-the-art Fully Convolutional Grasp Quality CNN, both on simulated and real camera images. The results show statistically significant improvements in terms of grasp success rate when using simulated images over real camera images, especially when the real camera viewpoint is angled. Code and video are available at https://irobotics.aalto.fi/beyond-top-grasps-through-scene-completion/.