Paper Info
Title
Multi-Stage Learning Of Selective Dual-Arm Grasping Based On Obtaining And Pruning Grasping Points Through The Robot Experience In The Real World
Abstract
Recently, self-supervised approach is common for robot grasping. Although this approach improves success rate, it requires a long time to execute a number of grasp trials, and single-arm grasping is only considered. However, robots can grasp more various objects with two arms, and dual-arm robots such as humanoid robots are expected to execute dual-arm manipulation and overcome the single-arm limitation.In this paper, we introduce dual-arm grasping as another possible strategy and propose a multi-stage learning method for selective dual-arm grasping using Convolutional Neural Networks (CNN) for grasping point prediction and semantic segmentation. In the first stage, the network learns grasping points with the automatic annotation. Although a robot learns both single-arm and dual-arm grasping efficiently with the annotation, the robot may not be able to grasp it because the annotation algorithm is designed by human. Therefore, for the second stage, the robot samples various grasping points with both grasping strategies and learns how to grasp in the real world. In this stage, the robot obtains new possible grasping points and prunes unsuccessful ones for both grasping strategies through the robot experience. In the experiments in the real world, the adapted network achieved high success rate 76.7% in 90 trials. Since the network trained with no adaptation stage resulted in lower success rate 56.7%, this result also shows the network was refined with less than 250 times of grasp sampling. As an application of our method, we demonstrated that our system worked well in warehouse picking task.
Year
DOI
Venue
2018
10.1109/IROS.2018.8593752
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
ISSN
Computer vision,GRASP,Task analysis,Convolutional neural network,Computer science,Segmentation,Image segmentation,Artificial intelligence,Robot,Semantics,Humanoid robot
Conference
2153-0858
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Shingo Kitagawa102.03
Kentaro Wada213.43
shun hasegawa373.84
Kei Okada4534118.08
Masayuki Inaba52186410.27