# Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty

arXiv: https://arxiv.org/abs/1608.02239 · HTML: https://mlsanity.com/paper/1608.02239/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2016.

## Built on by (most cited first, 14 of 14)

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- [FuncGrasp: Learning Object-Centric Neural Grasp Functions from Single Annotated Example Object](https://mlsanity.com/paper/2402.05644/) (2024)
- [Improving the Successful Robotic Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/2403.05211/) (2024)
- [Discovering Unsupervised Behaviours from Full-State Trajectories](https://mlsanity.com/paper/2211.15451/) (2022)
- [Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection](https://mlsanity.com/paper/2101.10226/) (2021)
- [DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future](https://mlsanity.com/paper/2105.13806/) (2021)
- [Data-efficient learning of object-centric grasp preferences](https://mlsanity.com/paper/2203.00384/) (2022)
- [PEGG-Net: Pixel-Wise Efficient Grasp Generation in Complex Scenes](https://mlsanity.com/paper/2203.16301/) (2022)
- [Accurate Ground-Truth Depth Image Generation via Overfit Training of Point Cloud Registration using Local Frame Sets](https://mlsanity.com/paper/2207.07016/) (2022)
- [Towards a Modular Bin-picking Framework for Handling Object Pose Uncertainties](https://mlsanity.com/paper/2607.13698/) (2026)

## Abstract (arXiv)

This paper presents a new method for parallel-jaw grasping of isolated objects from depth images, under large gripper pose uncertainty. Whilst most approaches aim to predict the single best grasp pose from an image, our method first predicts a score for every possible grasp pose, which we denote the grasp function. With this, it is possible to achieve grasping robust to the gripper's pose uncertainty, by smoothing the grasp function with the pose uncertainty function. Therefore, if the single best pose is adjacent to a region of poor grasp quality, that pose will no longer be chosen, and instead a pose will be chosen which is surrounded by a region of high grasp quality. To learn this function, we train a Convolutional Neural Network which takes as input a single depth image of an object, and outputs a score for each grasp pose across the image. Training data for this is generated by use of physics simulation and depth image simulation with 3D object meshes, to enable acquisition of sufficient data without requiring exhaustive real-world experiments. We evaluate with both synthetic and real experiments, and show that the learned grasp score is more robust to gripper pose uncertainty than when this uncertainty is not accounted for.