Real-Time Grasp Detection Using Convolutional Neural Networks
This addresses the problem of real-time and accurate grasp detection for robotics, with incremental improvements in speed and accuracy.
The paper tackles robotic grasp detection by introducing a convolutional neural network that performs single-stage regression to graspable bounding boxes, achieving a 14 percentage point improvement over state-of-the-art methods and running at 13 frames per second on a GPU.
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.