Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps

02/16/2018

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Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown objects. In this work we improve on one of the most promising approaches, the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We propose a new architecture for the GQ-CNN and describe practical improvements that increase the model validation accuracy from 92.2 to 88.0 splits.

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Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps

Learning to Grasp 3D Objects using Deep Residual U-Nets

VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction

Dictionary Learning for Robotic Grasp Recognition and Detection

Intent-based Object Grasping by a Robot using Deep Learning

Associating Grasp Configurations with Hierarchical Features in Convolutional Neural Networks

ECNNs: Ensemble Learning Methods for Improving Planar Grasp Quality Estimation

Domain Independent Unsupervised Learning to grasp the Novel Objects

Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps

Related Research

Learning to Grasp 3D Objects using Deep Residual U-Nets

VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction

Dictionary Learning for Robotic Grasp Recognition and Detection

Intent-based Object Grasping by a Robot using Deep Learning

Associating Grasp Configurations with Hierarchical Features in Convolutional Neural Networks

ECNNs: Ensemble Learning Methods for Improving Planar Grasp Quality Estimation

Domain Independent Unsupervised Learning to grasp the Novel Objects