Predicting Multiple Pregrasping Poses by Combining Deep Convolutional Neural Networks with Mixture Density Networks

Sungphill Moon, Youngbin Park, Il Hong Suh

Abstract: In this paper, we propose a deep neural network to predict the pregrasp poses of a three-dimensional (3D) object. Specifically, a single RGB-D image is used to determine multiple pregrasp position of three fingers of the robotic hand for various poses of known or unknown objects. Multiple pregrasping pose prediction typically involves the use of complex multi-valued functions where standard regression models fail. To this end, we propose a deep neural network containing a variant of the traditional deep convolutional neural network as well as a mixture density network. Furthermore, in order to overcome the difficulty of learning with insufficient data in the first part of the proposed network, we develop a supervised learning technique to pretrain the variant of the convolutional neural network.