A Novel Generative Convolutional Neural Network for Robot Grasp Detection on Gaussian Guidance

TL;DR

This paper introduces a new generative CNN model for robot grasp detection that uses Gaussian guidance and deformable convolutions to improve accuracy and reduce false positives in real-world scenarios.

Contribution

The paper presents a novel CNN architecture with Gaussian-based guided training and deformable convolutions for enhanced robot grasp detection accuracy.

Findings

Achieved 99.0% accuracy on Cornell dataset

Achieved 95.9% accuracy on Jacquard dataset

Demonstrated successful real-world robot grasping

Abstract

The vision-based grasp detection method is an important research direction in the field of robotics. However, due to the rectangle metric of the grasp detection rectangle's limitation, a false-positive grasp occurs, resulting in the failure of the real-world robot grasp task. In this paper, we propose a novel generative convolutional neural network model to improve the accuracy and robustness of robot grasp detection in real-world scenes. First, a Gaussian-based guided training method is used to encode the quality of the grasp point and grasp angle in the grasp pose, highlighting the highest-quality grasp point position and grasp angle and reducing the generation of false-positive grasps. Simultaneously, deformable convolution is used to obtain the shape features of the object in order to guide the subsequent network to the position. Furthermore, a global-local feature fusion method is introduced in order to efficiently obtain finer features during the feature reconstruction stage, allowing the network to focus on the features of the grasped objects. On the Cornell Grasping Datasets and Jacquard Datasets, our method achieves excellent performance of 99.0$\%$ and 95.9$\%$, respectively. Finally, the proposed method is put to the test in a real-world robot grasping scenario.