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Advances on Robotic Item Picking

Overview of attention for book
Cover of 'Advances on Robotic Item Picking'

Table of Contents

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    Book Overview
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    Chapter 1 Team CVAP’s Mobile Picking System at the Amazon Picking Challenge 2015
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    Chapter 2 Team UAlberta: Amazon Picking Challenge Lessons
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    Chapter 3 A Soft Robotics Approach to Autonomous Warehouse Picking
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    Chapter 4 Top-Down Approach to Object Identification in a Cluttered Environment Using Single RGB-D Image and Combinational Classifier for Team Nanyang’s Robotic Picker at the Amazon Picking Challenge 2015
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    Chapter 5 Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
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    Chapter 6 End-to-End Learning of Object Grasp Poses in the Amazon Robotics Challenge
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    Chapter 7 A Systems Engineering Analysis of Robot Motion for Team Delft’s APC Winner 2016
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    Chapter 8 Standing on Giant’s Shoulders: Newcomer’s Experience from the Amazon Robotics Challenge 2017
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    Chapter 9 Team C 2 M: Two Cooperative Robots for Picking and Stowing in Amazon Picking Challenge 2016
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    Chapter 10 Description of IITK-TCS System for ARC 2017
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    Chapter 11 Designing Cartman : A Cartesian Manipulator for the Amazon Robotics Challenge 2017
Attention for Chapter 10: Description of IITK-TCS System for ARC 2017
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Citations

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Chapter title
Description of IITK-TCS System for ARC 2017
Chapter number 10
Book title
Advances on Robotic Item Picking
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-35679-8_10
Book ISBNs
978-3-03-035678-1, 978-3-03-035679-8
Authors

Anima Majumder, Olyvia Kundu, Samrat Dutta, Swagat Kumar, Laxmidhar Behera, Majumder, Anima, Kundu, Olyvia, Dutta, Samrat, Kumar, Swagat, Behera, Laxmidhar

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 100%
Readers by discipline Count As %
Computer Science 1 100%

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