© 2020

Advances on Robotic Item Picking

Applications in Warehousing & E-Commerce Fulfillment

  • Albert Causo
  • Joseph Durham
  • Kris Hauser
  • Kei Okada
  • Alberto Rodriguez

Table of contents

  1. Front Matter
    Pages i-viii
  2. Kaiyu Hang, Francisco E. Viña B., Michele Colledanchise, Karl Pauwels, Alessandro Pieropan, Danica Kragic
    Pages 1-12
  3. Camilo Perez Quintero, Oscar Ramirez, Andy Hess, Rong Feng, Masood Dehghan, Hong Zhang et al.
    Pages 13-21
  4. Tommaso Pardi, Mattia Poggiani, Emanuele Luberto, Alessandro Raugi, Manolo Garabini, Riccardo Persichini et al.
    Pages 23-35
  5. Diogo Almeida, Rares Ambrus, Sergio Caccamo, Xi Chen, Silvia Cruciani, João F. Pinto B. De Carvalho et al.
    Pages 53-62
  6. Eiichi Matsumoto, Masaki Saito, Ayaka Kume, Jethro Tan
    Pages 63-72
  7. Carlos Hernandez Corbato, Mukunda Bharatheesha
    Pages 73-85
  8. Gustavo Alfonso Garcia Ricardez, Lotfi El Hafi, Felix von Drigalski
    Pages 87-100
  9. Hironobu Fujiyoshi, Takayoshi Yamashita, Shuichi Akizuki, Manabu Hashimoto, Yukiyasu Domae, Ryosuke Kawanishi et al.
    Pages 101-112
  10. Anima Majumder, Olyvia Kundu, Samrat Dutta, Swagat Kumar, Laxmidhar Behera
    Pages 113-124
  11. Jürgen Leitner, Douglas Morrison, Anton Milan, Norton Kelly-Boxall, Matthew McTaggart, Adam W. Tow et al.
    Pages 125-148
  12. Back Matter
    Pages 149-152

About this book


This book is a compilation of advanced research and applications on robotic item picking and warehouse automation for e-commerce applications. The works in this book are based on results that came out of the Amazon Robotics Challenge from 2015-2017, which focused on fully automated item picking in warehouse setting, a topic that has been assumed too complicated to solve or has been reduced to a more tractable form of bin picking or single-item table top picking. The book’s contributions reveal some of the top solutions presented from the 50 participant teams. Each solution works to address the time-constraint, accuracy, complexity, and other difficulties that come with warehouse item picking. The book covers topics such as grasping and gripper design, vision and other forms of sensing, actuation and robot design, motion planning, optimization, machine learning and artificial intelligence, software engineering, and system integration, among others. Through this book, the authors describe how robot systems are built from the ground up to do a specific task, in this case, item picking in a warehouse setting. The compiled works come from the best robotics research institutions and companies globally.

  • Presents an inside look at the various solutions for automated warehouse item picking based on the Amazon Robotics Challenge (ARC)
  • Contains details of the challenges and solutions involved in automating item picking
  • Provides details and insights on the solutions of the winning teams
  • Includes chapters written by scientists and engineers at the forefront of robotics research


Warehouse automation Robotic grasping Amazon Robotics Challenge Machine Learning for Object Identification Automated item picking Gripper design

Editors and affiliations

  • Albert Causo
    • 1
  • Joseph Durham
    • 2
  • Kris Hauser
    • 3
  • Kei Okada
    • 4
  • Alberto Rodriguez
    • 5
  1. 1.Robotics Research CentreNanyang Technological UnivSingaporeSingapore
  2. 2.Amazon Robotics LLCAmazon (United States)North ReadingUSA
  3. 3.Duke UniversityDurhamUSA
  4. 4.University of TokyoTokyoJapan
  5. 5.Massachusetts Institute of TechnologyCambridgeUSA

About the editors

Albert Causo is a Senior Research Fellow at the Robotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore. He obtained his masters and Ph.D. in Information Science from Nara Institute of Science and Technology in 2006 and 2010, respectively. His research interests include computer vision, human-robot interaction, human motion measurement, human posture tracking and modelling, rehabilitation robotics, robot-assisted education and, grasping strategy in item picking for professional services and logistics robot for warehouse fulfillment.

Joey Durham is Manager of Research and Advanced Development at Amazon Robotics. His team focuses on resource allocation algorithms, machine learning, and path planning for robotic warehouses. He also runs the Amazon Picking Challenge robotic manipulation contest. Joey joined Kiva Systems after completing his Ph.D. at the University of California at Santa Barbara in distributed coordination for teams of robots. He has been with the company through its acquisition and growth into Amazon Robotics. Previously he worked on path planning for autonomous vehicles at Stanford University for the DARPA Grand Challenge. 

Kris Hauser is an Associate Professor at the Pratt School of Engineering at Duke University with a joint appointment in the Electrical and Computer Engineering Department and the Mechanical Engineering and Materials Science Department. He received his PhD in Computer Science from Stanford University in 2008, bachelor's degrees in Computer Science and Mathematics from UC Berkeley in 2003, and worked as a postdoctoral fellow at UC Berkeley. He then joined the faculty at Indiana University from 2009-2014, where he started the Intelligent Motion Lab. He is a recipient of a Stanford Graduate Fellowship, Siebel Scholar Fellowship, and the NSF CAREER award. 

Kei Okada received the MS and the PhD in Information Engineering from The University of Tokyo in 1999 and 2002, respectively. From 2002 to 2006, he joined the Professional Programme for Strategic Software Project in The University Tokyo. Since March 2006, he has been an assistant professor in Creative Informatics at The University of Tokyo. His research interests include humanoids robots, real-time 3D computer vision, and recognition-action integrated system. He is a member of IEEE, Information Processing Society of Japan and the Robotics Society of Japan. 

Alberto Rodriguez graduated in Mathematics and Telecommunication Engineering, with honors, from the Universitat Politecnica de Catalunya (UPC) in Barcelona. He earned a Ph.D. in Robotics from Carnegie Mellon University under the supervision of Professor Matthew T. Mason. His thesis was entitled "Shape for Contact.” He is currently a Postdoctoral Associate at the Computer Science and Artificial Intelligence Laboratory at MIT. Alberto is the recipient of La Caixa and Caja Madrid Fellowships for graduate studies in the U.S., and the recipient of Best Student Paper Awards from conferences RSS 2011 and ICRA 2013. His main research interests are in robotic manipulation, mechanical design, and automation. His long-term research goal is to provide robots with enough sensing, reasoning and acting capabilities to reliably manipulate the environment.

Bibliographic information