Estimating Grasping Patterns from Images Using Finetuned Convolutional Neural Networks

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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
Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings
Subtitle of host publicationTAROS 2018
EditorsMaria Elena Giannaccini, Manuel Giuliani, Tareq Assaf
PublisherSpringer Nature
Number of pages12
ISBN (Print)9783319967271
Publication statusPublished - 26 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10965 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Convolutional Neural Networks (CNN)
  • Deep learning
  • Grasping patterns
  • Transfer learning


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