Mask-GD Segmentation Based Robotic Grasp Detection

TL;DR

MASK-GD is a novel robotic grasp detection method that uses segmented target object masks to improve accuracy in complex scenes, outperforming existing algorithms in cluttered environments.

Contribution

The paper introduces MASK-GD, a new grasp detection algorithm that leverages object masks to enhance detection accuracy in cluttered scenes, addressing background interference issues.

Findings

Comparable performance to state-of-the-art algorithms on standard datasets

Significantly better performance in complex scenes

Effective use of object masks for grasp detection

Abstract

The reliability of grasp detection for target objects in complex scenes is a challenging task and a critical problem that needs to be solved urgently in practical application. At present, the grasp detection location comes from searching the feature space of the whole image. However, the cluttered background information in the image impairs the accuracy of grasping detection. In this paper, a robotic grasp detection algorithm named MASK-GD is proposed, which provides a feasible solution to this problem. MASK is a segmented image that only contains the pixels of the target object. MASK-GD for grasp detection only uses MASK features rather than the features of the entire image in the scene. It has two stages: the first stage is to provide the MASK of the target object as the input image, and the second stage is a grasp detector based on the MASK feature. Experimental results demonstrate that MASK-GD's performance is comparable with state-of-the-art grasp detection algorithms on Cornell Datasets and Jacquard Dataset. In the meantime, MASK-GD performs much better in complex scenes.