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

arXiv: https://arxiv.org/abs/1802.05992 · HTML: https://mlsanity.com/paper/1802.05992/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT](https://mlsanity.com/paper/2508.08748/) (2025)
- [Tactile-based Reinforcement Learning for Adaptive Grasping under Observation Uncertainties](https://mlsanity.com/paper/2505.16167/) (2025)

## Abstract (arXiv)

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.