Robotic Grasp Detection using Deep Convolutional Neural Networks
This advances robotic manipulation by enabling more reliable and efficient grasping for robots in unstructured environments.
The paper tackles robotic grasp detection by predicting grasping poses for novel objects using RGB-D images, achieving 89.21% accuracy on the Cornell Grasp Dataset with real-time performance.
Deep learning has significantly advanced computer vision and natural language processing. While there have been some successes in robotics using deep learning, it has not been widely adopted. In this paper, we present a novel robotic grasp detection system that predicts the best grasping pose of a parallel-plate robotic gripper for novel objects using the RGB-D image of the scene. The proposed model uses a deep convolutional neural network to extract features from the scene and then uses a shallow convolutional neural network to predict the grasp configuration for the object of interest. Our multi-modal model achieved an accuracy of 89.21% on the standard Cornell Grasp Dataset and runs at real-time speeds. This redefines the state-of-the-art for robotic grasp detection.