Deep learning approaches to grasp synthesis: A review
Gras** is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
A survey on learning-based robotic gras**
Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic gras** and manipulation. Current trends and …
learning approaches for vision-based robotic gras** and manipulation. Current trends and …
Anygrasp: Robust and efficient grasp perception in spatial and temporal domains
As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as
humans. Our innate gras** system is prompt, accurate, flexible, and continuous across …
humans. Our innate gras** system is prompt, accurate, flexible, and continuous across …
Neural thompson sampling
Learning robust, real-time, reactive robotic gras**
We present a novel approach to perform object-independent grasp synthesis from depth
images via deep neural networks. Our generative gras** convolutional neural network …
images via deep neural networks. Our generative gras** convolutional neural network …
Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
Learning high-DOF reaching-and-gras** via dynamic representation of gripper-object interaction
Learning hand-eye coordination for robotic gras** with deep learning and large-scale data collection
We describe a learning-based approach to hand-eye coordination for robotic gras** from
monocular images. To learn hand-eye coordination for gras**, we trained a large …
monocular images. To learn hand-eye coordination for gras**, we trained a large …