Paper Info
Title
Attention based visual analysis for fast grasp planning with multi-fingered robotic hand.
Abstract
We present an attention based visual analysis framework to compute grasp-relevant information in order to guide grasp planning using a multi-fingered robotic hand. Our approach uses a computational visual attention model to locate regions of interest in a scene, and uses a deep convolutional neural network to detect grasp type and point for a sub-region of the object presented in a region of interest. We demonstrate the proposed framework in object grasping tasks, in which the information generated from the proposed framework is used as prior information to guide the grasp planning. Results show that the proposed framework can not only speed up grasp planning with more stable configurations, but also is able to handle unknown objects. Furthermore, our framework can handle cluttered scenarios. A new Grasp Type Dataset (GTD) that considers 6 commonly used grasp types and covers 12 household objects is also presented.
Year
Venue
Field
2018
arXiv: Robotics
Grasp planning,GRASP,Robotic hand,Convolutional neural network,Computer science,Visual attention,Human–computer interaction,Artificial intelligence,Region of interest,Deep learning,Machine learning,Speedup
DocType
Volume
Citations 
Journal
abs/1809.04226
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Zhen Deng101.01
Ge Gao215915.84
Simone Frintrop369542.88
Jianwei Zhang43517.45
Fuchun Sun52377225.80
Changshui Zhang65506323.40