Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter
Robotics
2019-04-25 v2 Computer Vision and Pattern Recognition
Machine Learning
Abstract
This paper focuses on robotic picking tasks in cluttered scenario. Because of the diversity of poses, types of stack and complicated background in bin picking situation, it is much difficult to recognize and estimate their pose before grasping them. Here, this paper combines Resnet with U-net structure, a special framework of Convolution Neural Networks (CNN), to predict picking region without recognition and pose estimation. And it makes robotic picking system learn picking skills from scratch. At the same time, we train the network end to end with online samples. In the end of this paper, several experiments are conducted to demonstrate the performance of our methods.
Cite
@article{arxiv.1904.07402,
title = {Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter},
author = {Quanquan Shao and Jie Hu and Weiming Wang and Yi Fang and Wenhai Liu and Jin Qi and Jin Ma},
journal= {arXiv preprint arXiv:1904.07402},
year = {2019}
}
Comments
6 pages, 7 figures, conference