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Antipodal Robotic Grasping using Deep Learning.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Antipodal Robotic Grasping using Deep Learning./
Author:	Joshi, Shirin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	72 p.
Notes:	Source: Masters Abstracts International, Volume: 82-03.
Contained By:	Masters Abstracts International82-03.
Subject:	Robotics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28088105
ISBN:	9798664739336

Antipodal Robotic Grasping using Deep Learning.
Joshi, Shirin.

Antipodal Robotic Grasping using Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 72 p.

Source: Masters Abstracts International, Volume: 82-03.

Thesis (M.S.)--Rochester Institute of Technology, 2020.

This item must not be sold to any third party vendors.

In this work, we discuss two implementations that predict antipodal grasps for novel objects: A deep Q-learning approach and a Generative Residual Convolutional Neural Network approach. We present a deep reinforcement learning based method to solve the problem of robotic grasping using visio-motor feedback. The use of a deep learning based approach reduces the complexity caused by the use of hand-designed features. Our method uses an off-policy reinforcement learning framework to learn the grasping policy. We use the double deep Q-learning framework along with a novel Grasp-Q-Network to output grasp probabilities used to learn grasps that maximize the pick success. We propose a visual servoing mechanism that uses a multi-view camera setup that observes the scene which contains the objects of interest. We performed experiments using a Baxter Gazebo simulated environment as well as on the actual robot. The results show that our proposed method outperforms the baseline Q-learning framework and increases grasping accuracy by adapting a multi-view model in comparison to a single-view model. The second method tackles the problem of generating antipodal robotic grasps for unknown objects from an n-channel image of the scene. We propose a novel Generative Residual Convolutional Neural Network (GR-ConvNet) model that can generate robust antipodal grasps from n-channel input at real-time speeds (20ms). We evaluate the proposed model architecture on standard dataset and previously unseen household objects. We achieved state-of-the-art accuracy of 97.7% on Cornell grasp dataset. We also demonstrate a 93.5% grasp success rate on previously unseen real-world objects.

ISBN: 9798664739336Subjects--Topical Terms:

561941
Robotics.
Subjects--Index Terms:

Antipodal grasping

Antipodal Robotic Grasping using Deep Learning.
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245 1 0 $a Antipodal Robotic Grasping using Deep Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 72 p.
500 $a Source: Masters Abstracts International, Volume: 82-03.
500 $a Advisor: Sahin, Ferat.
502 $a Thesis (M.S.)--Rochester Institute of Technology, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a In this work, we discuss two implementations that predict antipodal grasps for novel objects: A deep Q-learning approach and a Generative Residual Convolutional Neural Network approach. We present a deep reinforcement learning based method to solve the problem of robotic grasping using visio-motor feedback. The use of a deep learning based approach reduces the complexity caused by the use of hand-designed features. Our method uses an off-policy reinforcement learning framework to learn the grasping policy. We use the double deep Q-learning framework along with a novel Grasp-Q-Network to output grasp probabilities used to learn grasps that maximize the pick success. We propose a visual servoing mechanism that uses a multi-view camera setup that observes the scene which contains the objects of interest. We performed experiments using a Baxter Gazebo simulated environment as well as on the actual robot. The results show that our proposed method outperforms the baseline Q-learning framework and increases grasping accuracy by adapting a multi-view model in comparison to a single-view model. The second method tackles the problem of generating antipodal robotic grasps for unknown objects from an n-channel image of the scene. We propose a novel Generative Residual Convolutional Neural Network (GR-ConvNet) model that can generate robust antipodal grasps from n-channel input at real-time speeds (20ms). We evaluate the proposed model architecture on standard dataset and previously unseen household objects. We achieved state-of-the-art accuracy of 97.7% on Cornell grasp dataset. We also demonstrate a 93.5% grasp success rate on previously unseen real-world objects.
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653 $a Deep learning
653 $a Deep reinforcement learning
653 $a Machine learning
653 $a Robotic grasping
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793 $a English
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