DDGC: Generative Deep Dexterous Grasping in Clutter

TL;DR

This paper introduces DDGC, a fast generative method for multi-finger robotic grasping in cluttered scenes from RGB-D images, significantly improving speed and quality over previous approaches.

Contribution

The paper presents DDGC, a novel neural network that efficiently generates high-quality, collision-free multi-finger grasps in cluttered environments from a single RGB-D image.

Findings

DDGC outperforms baseline methods in grasp quality.

DDGC is 5 times faster than existing planners.

DDGC successfully removes clutter in real-world scenes.

Abstract

Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps. In this work we address such limitations by introducing DDGC, a fast generative multi-finger grasp sampling method that can generate high quality grasps in cluttered scenes from a single RGB-D image. DDGC is built as a network that encodes scene information to produce coarse-to-fine collision-free grasp poses and configurations. We experimentally benchmark DDGC against the simulated-annealing planner in GraspIt! on 1200 simulated cluttered scenes and 7 real world scenes. The results show that DDGC outperforms the baseline on synthesizing high-quality grasps and removing clutter while being 5 times faster. This, in turn, opens up the door for using multi-finger grasps in practical applications which has so far been limited due to the excessive computation time needed by other methods.