# Real-world Multi-object, Multi-grasp Detection

arXiv: https://arxiv.org/abs/1802.00520 · HTML: https://mlsanity.com/paper/1802.00520/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2018.

## Built on by (most cited first, 3 of 3)

- [NBMOD: Find It and Grasp It in Noisy Background](https://mlsanity.com/paper/2306.10265/) (2023)
- [Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection](https://mlsanity.com/paper/2101.10226/) (2021)
- [LocaliseBot: Multi-view 3D object localisation with differentiable rendering for robot grasping](https://mlsanity.com/paper/2311.08438/) (2023)

## Abstract (arXiv)

A deep learning architecture is proposed to predict graspable locations for robotic manipulation. It considers situations where no, one, or multiple object(s) are seen. By defining the learning problem to be classification with null hypothesis competition instead of regression, the deep neural network with RGB-D image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot. The method outperforms state-of-the-art approaches on the Cornell dataset with 96.0% and 96.1% accuracy on image-wise and object- wise splits, respectively. Evaluation on a multi-object dataset illustrates the generalization capability of the architecture. Grasping experiments achieve 96.0% grasp localization and 88.0% grasping success rates on a test set of household objects. The real-time process takes less than .25 s from image to plan.