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Deep Learning for Robot Perception and Cognition - Research Book

Deep Learning for Robot Perception and Cognition - Research Book

Essential Research Book in Deep Learning for Robot Perception and Cognition

Author(s) Name:  Alexandros Iosifidis, Anastasios Tefas

About the Book:

   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

    • Table of Contents

  • 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

    • ISBN:  9780323857871

    Publisher:  Elsevier

    Year of Publication:  2022

    Book Link:  Home Page Url