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Elsevier

Deep Learning for Robot Perception and Cognition

  • 1 Edición - 4 de febrero de 2022
  • Última edición
  • Editores: Alexandros Iosifidis, Anastasios Tefas
  • Idioma: Inglés

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 method… Leer más

Descripción

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.

Puntos claves

  • 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

De interès para

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

Índice

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

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 4 de febrero de 2022
  • Idioma: Inglés

Sobre los editores

AI

Alexandros Iosifidis

Alexandros Iosifidis is a Professor at Aarhus University, Denmark. He leads the Machine Learning and Computational Intelligence group at the Department of Electrical and Computer Engineering. He received his Ph.D. from the Department of Informatics at Aristotle University of Thessaloniki, Greece in 2014. He participated in more than 15 research and development projects financed by national and European funds.
Afiliaciones y experiencia
Aarhus University, Denmark

AT

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. 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.”
Afiliaciones y experiencia
Department of Informatics, Aristotle University of Thessaloniki

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