Object discovery and grasp detection with a shared convolutional neural network
Di Guo, Tao Kong, Fuchun Sun, Huaping Liu
Abstract
Grasp an object from a stack of objects in real-time is still a challenge in robotics. This requires the robot to have the ability of both fast object discovery and grasp detection: a target object should be picked out from the stack first and then a proper grasp configuration is applied to grasp the object. In this paper, we propose a shared convolutional neural network (CNN) which can simultaneously implement these two tasks in real-time. The processing speed of the model is about 100 frames per second on a GPU which largely satisfies the requirement. Meanwhile, we also establish a labeled RGBD dataset which contains scenes of stacked objects for robotic grasping. At last, we demonstrate the implementation of our shared CNN model on a real robotic platform and show that the robot can accurately discover a target object from the stack and successfully grasp it.
BibTeX
@inproceedings{icra2016_objectdiscoverya,
title = {Object discovery and grasp detection with a shared convolutional neural network},
author = {Di Guo and Tao Kong and Fuchun Sun and Huaping Liu},
booktitle = {ICRA 2016},
year = {2016}
}