Estimating Grasping Patterns from Images Using Finetuned Convolutional Neural Networks

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

1 Dyfyniadau(SciVal)


Identification of suitable grasping pattern for numerous objects is a challenging computer vision task. It plays a vital role in robotics where a robotic hand is used to grasp different objects. Most of the work done in the area is based on 3D robotic grippers. An ample amount of work could also be found on humanoid robotic hands. However, there is negligible work on estimating grasping patterns from 2D images of various objects. In this paper, we propose a novel method to learn grasping patterns from images and data recorded from a dataglove, provided by the TUB Dataset. Our network retrains, a pre-trained deep Convolutional Neural Network (CNN) known as AlexNet, to learn deep features from images that correspond to human grasps. The results show that there are some interesting grasping patterns which are learned. In addition, we use two methods, Support Vector Machines (SVM) and hotelling’s T2 test to demonstrate that the dataset does include distinctive grasps for different objects. The results show promising grasping patterns that resembles actual human grasps
Iaith wreiddiolSaesneg
TeitlTowards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings
Is-deitlTAROS 2018
GolygyddionMaria Elena Giannaccini, Manuel Giuliani, Tareq Assaf
CyhoeddwrSpringer Nature
Nifer y tudalennau12
ISBN (Argraffiad)9783319967271
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 26 Awst 2018

Cyfres gyhoeddiadau

EnwLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Cyfrol10965 LNAI
ISSN (Argraffiad)0302-9743
ISSN (Electronig)1611-3349

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