Deep Learning for Robot Perception and Cognition

Deep Learning for Robot Perception and Cognition

1st Edition - February 1, 2022
  • Editors: Alexandros Iosifidis, Anastasios Tefas
  • Paperback ISBN: 9780323857871

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Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks.

Key Features

  • Presents deep learning principles and methodologies
  • Explains the principles of applying end-to-end learning in robotics applications
  • Presents how to design and train deep learning models
  • Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more
  • Uses robotic simulation environments for training deep learning models
  • Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis


Graduate students, university and industry researchers, practitioners in robot vision, Intelligent Control, and mechatronics

Table of Contents

  • 1. Introduction
    2. Neural Networks and Backpropagation
    3. Convolutional Neural Networks
    4. Graph Convolutional Networks
    5. Recurrent Neural Networks
    6. Deep Reinforcement Learning
    7. Lightweight Deep Learning
    8. Knowledge Distillation
    9. Progressive and Compressive Deep Learning
    10. Representation Learning and Retrieval
    11. Object Detection and Tracking
    12. Semantic Scene Segmentation for Robotics
    13. 3D Object Detection and Tracking
    14. Human Activity Recognition
    15. Deep Learning for Vision-based Navigation in Autonomous Drone Racing
    16. Robotic Grasping in Agile Production
    17. Deep learning in Multiagent Systems
    18. Simulation Environments
    19. Biosignal time-series analysis
    20. Medical Image Analysis
    21. Deep learning for robotics examples using OpenDR

Product details

  • No. of pages: 634
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: February 1, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323857871

About the Editors

Alexandros Iosifidis

Alexandros Iosifidis is an Associate Professor at Aarhus University, Denmark. He leads the Machine Learning and Computational Intelligence group at the Section of Electrical and Computer Engineering, Department of Engineering. He received his PhD from the Department of Informatics at Aristotle University of Thessaloniki, Greece in 2014. He has held Postdoctoral Researcher positions at Tampere University of Technology, Finland and Aristotle University of Thessaloniki, Greece. He received prestigious awards, including the Academy of Finland Postdoc Fellowship and the H.C. Ørsted Forskerspirer Prize for research excellence at a young age. He has contributed in twenty-two R&D projects financed by EU, Greek, Finnish, and Danish funding agencies and companies. He has (co-)authored 71 journal articles, 93 papers/abstracts and contributed 4 chapters to edited books in his area of expertise. His work has received 2250+ citations with h-index 26+ according to Google Scholar. Dr. Iosifidis is a Senior Member of IEEE and he served as an Officer of the Finnish IEEE Signal Processing-Circuits and Systems Chapter from 2016 to 2018. As of 2020 he serves as a member of the Technical Area Committee on Visual Information Processing of EURASIP. He is currently serving as an Associate Editor for the Neurocomputing, IEEE Access, BMC Bioinformatics, and PLOS One journals and as an Area Editor of the Signal Processing: Image Communication journal. He contributed in the organization of several international conferences as an Area/Technical Committee Chair, including IEEE ICIP (2018,2019,2020), EUSIPCO (2019) and IEEE ICASSP (2019).

Affiliations and Expertise

Associate Professor, Aarhus University, Denmark

Anastasios Tefas

Anastasios Tefas received the B.Sc. in informatics in 1997 and the Ph.D. degree in informatics in 2002, both from the Aristotle University of Thessaloniki, Greece. Since 2017 he has been an Associate Professor at the Department of Informatics, Aristotle University of Thessaloniki. From 2008 to 2017, he was a Lecturer, Assistant Professor at the same University. From 2006 to 2008, he was an Assistant Professor at the Department of Information Management, Technological Institute of Kavala. From 2003 to 2004, he was a temporary lecturer in the Department of Informatics, University of Thessaloniki. From 1997 to 2002, he was a researcher and teaching assistant in the Department of Informatics, University of Thessaloniki. Dr. Tefas participated in 20 research projects financed by national and European funds. He is the Coordinator of the H2020 project OpenDR, “Open Deep Learning Toolkit for Robotics”. He is Area Editor in Signal Processing: Image Communications journal. He has co-authored 120 journal papers, 235 papers in international conferences and contributed 8 chapters to edited books in his area of expertise. Over 5900 citations have been recorded to his publications and his H-index is 38 according to Google scholar. His current research interests include computational intelligence, deep learning, pattern recognition, machine learning, digital signal and image analysis and retrieval, computer vision and robotics.

Affiliations and Expertise

Thessaloniki, Greece