抓住
人工智能
卷积神经网络
计算机科学
深度学习
计算机视觉
RGB颜色模型
机器人学
人工神经网络
目标检测
机器人
模式识别(心理学)
程序设计语言
作者
Sulabh Kumra,Christopher Kanan
标识
DOI:10.1109/iros.2017.8202237
摘要
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.
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