Deep Learning for Robot Perception and Cognition

Alexandros Iosifidis, Anastasios Tefas

• Publisher: Academic Press
• Year: 2022
• Print ISBN: 9780323857871
• eBook ISBN: 9780323885720
• Topic: Technology & Engineering
• Subtopic: Automation in Engineering
• Index: Technology & Engineering

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. - 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

Table of contents

• Title of Book
• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• Preface
• Acknowledgements
• Editors biographies
• Chapter 1 Introduction
• Chapter 2 Neural networks and backpropagation
• Chapter 3 Convolutional neural networks
• Chapter 4 Graph convolutional networks
• Chapter 5 Recurrent neural networks
• Chapter 6 Deep reinforcement learning
• Chapter 7 Lightweight deep learning
• Chapter 8 Knowledge distillation
• Chapter 9 Progressive and compressive learning
• Chapter 10 Representation learning and retrieval
• Chapter 11 Object detection and tracking
• Chapter 12 Semantic scene segmentation for robotics
• Chapter 13 3D object detection and tracking
• Chapter 14 Human activity recognition
• Chapter 15 Deep learning for vision-based navigation in autonomous drone racing
• Chapter 16 Robotic grasping in agile production
• Chapter 17 Deep learning in multiagent systems
• Chapter 18 Simulation environments
• Chapter 19 Biosignal time-series analysis
• Chapter 20 Medical image analysis
• Chapter 21 Deep learning for robotics examples using OpenDR
• Index