Artificial Vision and Language Processing for Robotics: Create end-to-end systems that can power robots with artificial vision and deep learning techniques

✍ Scribed by Alvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre

• Publisher: Packt Publishing
• Year: 2019
• Tongue: English
• Leaves: 356
• Category: Library

✦ Synopsis

Create end-to-end systems that can power robots with artificial vision and deep learning techniques

Key Features

• Study ROS, the main development framework for robotics, in detail
• Learn all about convolutional neural networks, recurrent neural networks, and robotics
• Create a chatbot to interact with the robot

Book Description

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.

What you will learn

• Explore the ROS and build a basic robotic system
• Understand the architecture of neural networks
• Identify conversation intents with NLP techniques
• Learn and use the embedding with Word2Vec and GloVe
• Build a basic CNN and improve it using generative models
• Use deep learning to implement artificial intelligence(AI)and object recognition
• Develop a simple object recognition system using CNNs
• Integrate AI with ROS to enable your robot to recognize objects

Who this book is for

Artificial Vision and Language Processing for Robotics is for robotics engineers who want to learn how to integrate computer vision and deep learning techniques to create complete robotic systems. It will prove beneficial to you if you have working knowledge of Python and a background in deep learning. Knowledge of the ROS is a plus.

Table of Contents

1. Fundamentals of Robotics
2. Introduction to Computer Vision
3. Fundamentals of Natural Language Processing
4. Neural Networks with NLP
5. Convolutional Neural Networks
6. Robot Operating System
7. Build a Conventional Agent to Manage the Robot
8. Object Recognition to Guide a Robot Using CNNs
9. Computer Vision for Robotics

✦ Table of Contents

Table of Contents
Preface
Fundamentals of Robotics
Introduction
History of Robotics
Artificial Intelligence
Natural Language Processing
Computer Vision
Types of Robots
Industrial Robots
Service Robots
Hardware and Software of Robots
Robot Positioning
Exercise 1: Computing a Robot’s Position
How to Work with Robots
Exercise 2: Computing the Distance Traveled by a Wheel with Python
Exercise 3: Computing Final Position with Python
Activity 1: Robot Positioning Using Odometry with Python
Summary
Introduction to Computer Vision
Introduction
Basic Algorithms in Computer Vision
Image Terminology
OpenCV
Basic Image Processing Algorithms
Thresholding
Exercise 4: Applying Various Thresholds to an Image
Morphological Transformations
Exercise 5: Applying the Various Morphological Transformations to an Image
Blurring (Smoothing)
Exercise 6: Applying the Various Blurring Methods to an Image
Exercise 7: Loading an Image and Applying the Learned Methods
Introduction to Machine Learning
Decision Trees and Boosting Algorithms
Bagging:
Boosting
Exercise 8: Predicting Numbers Using the Decision Tree, Random Forest, and AdaBoost Algorithms
Artificial Neural Networks (ANNs)
Exercise 9: Building Your First Neural Network
Activity 2: Classify 10 Types of Clothes from the Fashion-MNIST Database
Summary
Fundamentals of Natural Language Processing
Introduction
Natural Language Processing
Parts of NLP
Levels of NLP
NLP in Python
Natural Language Toolkit (NLTK)
Exercise 10: Introduction to NLTK
spaCy
Exercise 11: Introduction to spaCy
Topic Modeling
Term Frequency – Inverse Document Frequency (TF-IDF)
Latent Semantic Analysis (LSA)
Exercise 12: Topic Modeling in Python
Activity 3: Process a Corpus
Language Modeling
Introduction to Language Models
The Bigram Model
N-gram Model
Calculating Probabilities
Exercise 13: Create a Bigram Model
Summary
Neural Networks with NLP
Introduction
Recurrent Neural Networks
Introduction to Recurrent Neural Networks (RNN)
Inside Recurrent Neural Networks
RNN architectures
Long-Dependency Problem
Exercise 14: Predict House Prices with an RNN
Long Short-Term Memory
Exercise 15: Predict the Next Solution of a Mathematical Function
Neural Language Models
Introduction to Neural Language Models
RNN Language Model
Exercise 16: Encoding a Small Corpus
The Input Dimensions of RNNs
Activity 4: Predict the Next Character in a Sequence
Summary
Convolutional Neural Networks for Computer Vision
Introduction
Fundamentals of CNNs
Building Your First CNN
Exercise 17: Building a CNN
Improving Your Model - Data Augmentation
Exercise 18: Improving Models Using Data Augmentation
Activity 5: Making Use of Data Augmentation to Classify correctly Images of Flowers
State-of-the-Art Models - Transfer Learning
Exercise 19: Classifying €5 and €20 Bills Using Transfer Learning with Very Little Data
Summary
Robot Operating System (ROS)
Introduction
ROS Concepts
ROS Commands
Installation and Configuration
Catkin Workspaces and Packages
Publishers and Subscribers
Exercise 20: Publishing and Subscribing
Exercise 21: Publishers and Subscribers
Simulators
Exercise 22: The Turtlebot configuration
Exercise 23: Simulators and Sensors
Activity 6: Simulators and Sensors
Summary
Build a Text-Based Dialogue System (Chatbot)
Introduction
Word Representation in Vector Space
Word Embeddings
Cosine Similarity
Word2Vec
Problems with Word2Vec
Gensim
Exercise 24: Creation of a Word Embedding
Global Vectors (GloVe)
Exercise 25: Using a Pretrained GloVe to See the Distribution of Words in a Plane
Dialogue Systems
Tools for Developing Chatbots
Types of Conversational Agents
Classification by Input-Output Data Type
Classification by System Knowledge
Creation of a Text-Based Dialogue System
Exercise 26: Create Your First Conversational Agent
Activity 7: Create a Conversational Agent to Control a Robot
Summary
Object Recognition to Guide a Robot Using CNNs
Introduction
Multiple Object Recognition and Detection
Exercise 24: Building Your First Multiple Object Detection and Recognition Algorithm
ImageAI
Multiple Object Recognition and Detection in Video
Activity 8: Multiple Object Detection and Recognition in Video
Summary
Computer Vision for Robotics
Introduction
Darknet
Basic Installation of Darknet
YOLO
First Steps in Image Classification with YOLO
YOLO on a Webcam
Exercise 28: Programming with YOLO
ROS Integration
Exercise 29: ROS and YOLO Integration
Activity 9: A Robotic Security Guard
Summary
Appendix
Index
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