Book Image

Artificial Vision and Language Processing for Robotics

By : Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre
Book Image

Artificial Vision and Language Processing for Robotics

By: Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre

Overview of this book

Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video. By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment.

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Table of Contents (12 chapters)
Artificial Vision and Language Processing for Robotics
Artificial Vision and Language Processing for Robotics
Preface
Preface
Free Chapter
1
Fundamentals of Robotics
Fundamentals of Robotics
Introduction
History of Robotics
Artificial Intelligence
Robot Positioning
Summary
2
Introduction to Computer Vision
Introduction to Computer Vision
Introduction
Basic Algorithms in Computer Vision
Introduction to Machine Learning
Summary
3
Fundamentals of Natural Language Processing
Fundamentals of Natural Language Processing
Introduction
NLP in Python
Topic Modeling
Language Modeling
Summary
4
Neural Networks with NLP
Neural Networks with NLP
Introduction
Recurrent Neural Networks
Long Short-Term Memory
Neural Language Models
Summary
5
Convolutional Neural Networks for Computer Vision
Convolutional Neural Networks for Computer Vision
Introduction
Fundamentals of CNNs
Building Your First CNN
Improving Your Model - Data Augmentation
State-of-the-Art Models - Transfer Learning
Summary
6
Robot Operating System (ROS)
Robot Operating System (ROS)
Introduction
ROS Concepts
ROS Commands
Installation and Configuration
Catkin Workspaces and Packages
Publishers and Subscribers
Simulators
Summary
7
Build a Text-Based Dialogue System (Chatbot)
Build a Text-Based Dialogue System (Chatbot)
Introduction
Word Representation in Vector Space
Dialogue Systems
Summary
8
Object Recognition to Guide a Robot Using CNNs
Object Recognition to Guide a Robot Using CNNs
Introduction
Multiple Object Recognition and Detection
Multiple Object Recognition and Detection in Video
Summary
9
Computer Vision for Robotics
Computer Vision for Robotics
Introduction
Darknet
YOLO
Summary
Appendix
Appendix
Chapter 1: Fundamentals of Robotics
Chapter 2: Introduction to Computer Vision
Chapter 3: Fundamentals of Natural Language Processing
Chapter 4: Neural Networks with NLP
Chapter 5: Convolutional Neural Networks for Computer Vision
Chapter 6: Robot Operating System (ROS)
Chapter 7: Build a Text-Based Dialogue System (Chatbot)
Chapter 8: Object Recognition to Guide a Robot Using CNNs
Chapter 9: Computer Vision for Robotics

Long Short-Term Memory

LSTM is a type of RNN that's designed to solve the long-dependency problem. It can remember values for long or short time periods. The principal way it differs from traditional RNNs is that they include a cell or a loop to store the memory internally.

This type of neural network was created in 1997 by Hochreiter and Schmidhuber. This is the basic schema of an LSTM neuron:

Figure 4.12: LSTM neuron structure

As you can see in the previous figure, the schema of an LSTM neuron is complex. It has three types of gate:

  • Input gate: Allows us to control the input values to update the state of the memory cell.

  • Forget gate: Allows us to erase the content of the memory cell.

  • Output gate: Allows us to control the returned values of the input and cell memory content.

An LSTM model in Keras has a three-dimensional input:

  • Sample: Is the amount of data you have (quantity of sequences).

  • Time step: Is the memory of your network. In other words, it stores previous information in order to make...

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