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

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