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.
Table of Contents (12 chapters)
Artificial Vision and Language Processing for Robotics
Preface

NLP in Python


Python has become very popular in recent years, by combining the power of general-purpose programming languages with the use of specific domain languages, such as MATLAB and R (designed for mathematics and statistics). It has different libraries for data loading, visualization, NLP, image processing, statistics, and more. Python has the most powerful libraries for text processing and machine learning algorithms.

Natural Language Toolkit (NLTK)

NLTK is the most common kit of tools for working with human language data in Python. It includes a set of libraries and programs for processing natural language and statistics. NLTK is commonly used as a learning tool and for carrying out research.

This library provides interfaces and methods for over 50 corpora and lexical resources. NLTK is capable of classifying text and performing other functions, such as tokenization, stemming (extracting the stem of a word), tagging (identifying the tag of a word, such as person, city…), and parsing...