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

Introduction


One of the latest trends in deep NLP is the creation of conversational agents, also knowns as chatbots. A chatbot is a text-based dialogue system that understands human language and can hold a real conversation with people. Many companies use these systems to interact with its customers to obtain information and feedback, for example, opinions on a new product launch.

Chatbots are used as assistants, for example, Siri, Alexa, and Google Home. These can give us real-time information about the weather or traffic.

At this point, the question is how can bots understand us? In the previous chapters, we have reviewed language models and how they work. However, the most important thing in language models (LMs) is the position of a word in a sentence. Each word has a certain probability of appearing in a sentence, depending on the words already in that sentence. But the probability distribution approach is not a good fit for this task. In this case, we need to understand the meaning...