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

Frontiers in Neurorobotics

Fuchun Sun¹  Changshui Zhang¹  Simone Frintrop²  Ge Gao²  Zhen Deng²  Jianwei Zhang² 

[1] Department of Computer Science and Technology, Tsinghua University, Beijing, China;Department of Informatics, University of Hamburg, Hamburg, Germany;

关键词: grasp planning;    grasp type;    visual attention;    deep learning;    multi-fingered robotic hand;   

DOI  :  10.3389/fnbot.2019.00060

来源: DOAJ

【 摘 要 】

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 planning with more stable configurations, but also handle unknown objects. Furthermore, our framework can handle cluttered scenarios. A new Grasp Type Dataset (GTD) which includes six commonly used grasp types and covers 12 household objects is also presented.

【 授权许可】

Unknown