Robotic grasp detection using deep convolutional neural networks
Sulabh Kumra,Christopher Kanan +1 more
- pp 769-776
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TLDR
In this article, the authors used a deep convolutional neural network to extract features from the scene and then used a shallow CNN to predict the grasp configuration for the object of interest.Abstract:
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.read more
Citations
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Journal ArticleDOI
Real-World Multiobject, Multigrasp Detection
TL;DR: A deep learning architecture is proposed to predict graspable locations for robotic manipulation by defining the learning problem to be classified with null hypothesis competition instead of regression, the deep neural network with red, green, blue and depth image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot.
Journal ArticleDOI
Learning robust, real-time, reactive robotic grasping:
TL;DR: A novel approach to perform object-independent grasp synthesis from depth images via deep neural networks overcomes shortcomings in existing techniques, namely discrete sampling of grasp candidates and long computation times, and achieves better performance, particularly in clutter.
Proceedings ArticleDOI
Fully Convolutional Grasp Detection Network with Oriented Anchor Box
TL;DR: In this article, an end-to-end fully convolutional neural network is employed to predict multiple grasping poses for a parallel-plate robotic gripper using RGB images, which achieves an accuracy of 97.74% and 96.61% on image-wise and object-wise split respectively.
Journal ArticleDOI
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
TL;DR: Three key tasks during vision-based robotic grasping are concluded, which are object localization, object pose estimation and grasp estimation, which include 2D planar grasp methods and 6DoF grasp methods.
Journal ArticleDOI
A Survey on Learning-Based Robotic Grasping
TL;DR: This review provides a comprehensive overview of machine learning approaches for vision-based robotic grasping and manipulation and gives an overview of techniques and achievements in transfers from simulations to the real world.
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