1 research outputs found
Attention based visual analysis for fast grasp planning with multi-fingered robotic hand
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