# GP-net: Flexible Viewpoint Grasp Proposal

arXiv: https://arxiv.org/abs/2209.10404 · HTML: https://mlsanity.com/paper/2209.10404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 21 of 21)

- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects](https://mlsanity.com/paper/2110.14217/) (2021)
- [ACRONYM: A Large-Scale Grasp Dataset Based on Simulation](https://mlsanity.com/paper/2011.09584/) (2020)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Grasp Pose Detection in Point Clouds](https://mlsanity.com/paper/1706.09911/) (2017)
- [Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations](https://mlsanity.com/paper/2104.01542/) (2021)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter](https://mlsanity.com/paper/2101.01132/) (2021)
- [Shape Completion Enabled Robotic Grasping](https://mlsanity.com/paper/1609.08546/) (2016)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation](https://mlsanity.com/paper/2003.01314/) (2020)
- [Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network](https://mlsanity.com/paper/1909.04810/) (2019)
- [Multimodal Deep Learning for Robust RGB-D Object Recognition](https://mlsanity.com/paper/1507.06821/) (2015)
- [Learning a visuomotor controller for real world robotic grasping using simulated depth images](https://mlsanity.com/paper/1706.04652/) (2017)
- [Beyond Top-Grasps Through Scene Completion](https://mlsanity.com/paper/1909.12908/) (2019)
- [Robot Learning of 6 DoF Grasping using Model-based Adaptive Primitives](https://mlsanity.com/paper/2103.12810/) (2021)
- [TransSC: Transformer-based Shape Completion for Grasp Evaluation](https://mlsanity.com/paper/2107.00511/) (2021)
- [Volumetric-based Contact Point Detection for 7-DoF Grasping](https://mlsanity.com/paper/2209.06675/) (2022)
- [VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction](https://mlsanity.com/paper/2203.04874/) (2022)

## Abstract (arXiv)

We present the Grasp Proposal Network (GP-net), a Convolutional Neural Network model which can generate 6-DoF grasps from flexible viewpoints, e.g. as experienced by mobile manipulators. To train GP-net, we synthetically generate a dataset containing depth-images and ground-truth grasp information. In real-world experiments, we use the EGAD evaluation benchmark to evaluate GP-net against two commonly used algorithms, the Volumetric Grasping Network (VGN) and the Grasp Pose Detection package (GPD), on a PAL TIAGo mobile manipulator. In contrast to the state-of-the-art methods in robotic grasping, GP-net can be used for grasping objects from flexible, unknown viewpoints without the need to define the workspace and achieves a grasp success of 54.4% compared to 51.6% for VGN and 44.2% for GPD. We provide a ROS package along with our code and pre-trained models at https://aucoroboticsmu.github.io/GP-net/.