Skip to main content

Research Repository

Advanced Search

Evaluating Gaussian Grasp Maps for Generative Grasping Models

Prew, W.; Breckon, T.P.; Bordewich, M.J.R.; Beierholm, U.

Evaluating Gaussian Grasp Maps for Generative Grasping Models Thumbnail



Generalising robotic grasping to previously unseen objects is a key task in general robotic manipulation. The current method for training many antipodal generative grasping models rely on a binary ground truth grasp map generated from the centre thirds of correctly labelled grasp rectangles. However, these binary maps do not accurately reflect the positions in which a robotic arm can correctly grasp a given object. We propose a continuous Gaussian representation of annotated grasps to generate ground truth training data which achieves a higher success rate on a simulated robotic grasping benchmark. Three modern generative grasping networks are trained with either binary or Gaussian grasp maps, along with recent advancements from the robotic grasping literature, such as discretisation of grasp angles into bins and an attentional loss function. Despite negligible difference according to the standard rectangle metric, Gaussian maps better reproduce the training data and therefore improve success rates when tested on the same simulated robot arm by avoiding collisions with the object: achieving 87.94% accuracy. Furthermore, the best performing model is shown to operate with a high success rate when transferred to a real robotic arm, at high inference speeds, without the need for transfer learning. The system is then shown to be capable of performing grasps on an antagonistic physical object dataset benchmark.


Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2022). Evaluating Gaussian Grasp Maps for Generative Grasping Models.

Conference Name Proc. Int. Joint Conf. Neural Networks
Conference Location Padova, Italy
Start Date Jul 18, 2022
End Date Jul 23, 2022
Acceptance Date Apr 26, 2022
Online Publication Date Jul 18, 2022
Publication Date 2022-07
Deposit Date May 31, 2022
Publicly Available Date Jun 6, 2022
Publisher Institute of Electrical and Electronics Engineers
Publisher URL


Accepted Conference Proceeding (3.2 Mb)

Publisher Licence URL

Copyright Statement
This work was funded by UKRI EPSRC. For the purpose of
open access, the authors have applied a Creative Commons
Attribution (CC BY) license to the Accepted Manuscript
version arising.

You might also like

Downloadable Citations