Real-Time Grasp Detection Using Convolutional Neural Networks
Venue
International Conference on Robotics and Automation (ICRA), IEEE (2015)
Publication Year
2015
Authors
Joseph Redmon, Anelia Angelova
BibTeX
Abstract
We present an accurate, real-time approach to robotic grasp detection based on
convolutional neural networks. Our network performs single-stage regression to
graspable bounding boxes without using standard sliding window or region proposal
techniques. The model outperforms state-of- the-art approaches by 14 percentage
points and runs at 13 frames per second on a GPU. Our network can simultaneously
perform classification so that in a single step it recognizes the object and finds
a good grasp rectangle. A modification to this model predicts multiple grasps per
object by using a locally constrained prediction mechanism. The locally constrained
model performs significantly better, especially on objects that can be grasped in a
variety of ways.
