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Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps
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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 95.8% and from 85.9% to 88.0% on respectively image-wise and object-wise training and validation splits.
Forward citations
Cited by 2 Pith papers
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Tactile-based Reinforcement Learning for Adaptive Grasping under Observation Uncertainties
A PPO-trained grasping policy using downsampled tactile feedback improves simulated grasp success by 3 to 11 percentage points across four object shapes under pose observation noise.
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Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
A pick-and-place system overlays bounding boxes on camera images, trains an ACT transformer on human demonstrations, and reports 80% to 100% success rates across three retail scenarios.
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