Attention Based Visual Analysis for Fast Grasp Planning With a Multi-Fingered Robotic Hand.

ABSTRACT: We present an attention based visual analysis framework to compute grasp-relevant information which helps 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 employ a deep convolutional neural network to detect grasp type and grasp attention point for a sub-region of the object in a region of interest. We demonstrate the proposed framework with object grasping tasks, in which the information generated from the proposed framework is used as prior information to guide grasp planning. The effectiveness of the proposed approach is evaluated in both simulation experiments and real-world experiments. Experimental results show that the proposed framework can not only speed up grasp plann

SUBMITTER: Deng Z 

PROVIDER: S-EPMC6684762 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature