DeepAI
Log In Sign Up

End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB

07/12/2021
by   Stefan Ainetter, et al.
0

In this work, we introduce a novel, end-to-end trainable CNN-based architecture to deliver high quality results for grasp detection suitable for a parallel-plate gripper, and semantic segmentation. Utilizing this, we propose a novel refinement module that takes advantage of previously calculated grasp detection and semantic segmentation and further increases grasp detection accuracy. Our proposed network delivers state-of-the-art accuracy on two popular grasp dataset, namely Cornell and Jacquard. As additional contribution, we provide a novel dataset extension for the OCID dataset, making it possible to evaluate grasp detection in highly challenging scenes. Using this dataset, we show that semantic segmentation can additionally be used to assign grasp candidates to object classes, which can be used to pick specific objects in the scene.

READ FULL TEXT

page 1

page 6

05/03/2019

Seamless Scene Segmentation

In this work we introduce a novel, CNN-based architecture that can be tr...
11/22/2021

Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks

In this paper, we present a novel deep neural network architecture for j...
01/18/2023

Model-based inexact graph matching on top of CNNs for semantic scene understanding

Deep learning based pipelines for semantic segmentation often ignore str...
07/15/2020

End-to-end training of a two-stage neural network for defect detection

Segmentation-based, two-stage neural network has shown excellent results...
10/07/2020

Toward Stance-based Personas for Opinionated Dialogues

In the context of chit-chat dialogues it has been shown that endowing sy...
10/21/2020

A Coarse-To-Fine (C2F) Representation for End-To-End 6-DoF Grasp Detection

We proposed an end-to-end grasp detection network, Grasp Detection Netwo...