Artificial Vision and Language Processing for Robotics
Create end-to-end systems that can power robots with artificial vision and deep learning techniques
Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre
- 356 pagine
- English
- ePUB (disponibile sull'app)
- Disponibile su iOS e Android
Artificial Vision and Language Processing for Robotics
Create end-to-end systems that can power robots with artificial vision and deep learning techniques
Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre
Informazioni sul libro
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.
Domande frequenti
Informazioni
Chapter 1
Fundamentals of Robotics
Learning Objectives
- Describe important events in the history of robotics
- Explain the importance of using artificial intelligence, artificial vision and natural language processing
- Classify a robot depending on its goal or function
- Identify the parts of a robot
- Estimate a robot’s position using odometry
Introduction
History of Robotics
Figure 1.1: History of robotics
Figure 1.2: History of robotics continued
Artificial Intelligence
- Siri: This is a voice assistant created by Apple, and is included in their phones and tablets. Siri is very useful as it is connected to the internet, allowing it to look up data instantly, send messages, check the weather, and do much more.
- Netflix: Netflix is an online film and TV service. It runs on a very accurate recommendation system that is developed using AI that recommends films to users based on their viewing history. For example, if a user usually watches romantic movies, the system will recommend romantic series and movies.
- Spotify: Spotify is an online music service similar to Netflix. It uses a recommendation system to make accurate song suggestions to users. To do so, it considers songs that the user has previously heard and the kind of music added to the user’s library.
- Tesla's self-driving cars: These cars are built using AI that can detect obstacles, people, and even traffic signals to ensure the passengers have a secure ride.
- Pacman: Like almost any other video game, Pacman’s enemies are programmed using AI. They use a specific technique that constantly computes the collision distance, taking into account wall boundaries, and they try to trap Pacman. As it is a very simple game, the algorithm is not very complex, but it is a good example that highlights the importance of AI in entertainment.
Natural Language Processing
- Siri: Apple’s voice assistant, Siri, uses NLP to understand what the user says and gives back a meaningful response.
- Cortana: This is another voice assistant that was created by Microsoft and is included in the Windows 10 operating system. It works in a similar way to Siri.
- Bixby: Bixby is a part of Samsung that is integrated in the newest Samsung phones, and its user experience is similar to using Siri or Cortana.
Note
You may be asking which one of these three is the best; however, it depends on each user’s likes and dislikes. - Phone operators: Nowadays, calls to customer services are commonly answered by answering machines. Most of these machines are phone operators that work by receiving a keyword input. Most modern operators are developed using NLP in order to have more realistic conversations with clients over the phone.
- Google Home: Google’s virtual home assistant uses NLP to respond to users’ questions and to perform given tasks.
Computer Vision
- Autonomous cars: Autonomous cars use computer vision to obtain traffic and environment information and to decide what to do on the basis of this information. For example, the car would stop if it captures a crossing pedestrian in its camera.
- Phone camera applications: Many phone-based camera applications include effects that modify a picture taken using the camera. For example, Instagram allows the user to use filters in real time that modify the image by mapping the user’s face to the filter.
- Tennis Hawk-Eye: This is a computer-based vision system used in tennis to track the trajectory of the ball and display its most likely path on the court. It is used to check whether the ball has bounced within the court’s boundaries.
Types of Robots
Industrial Robots
Service Robots
- Personal robots: These are commonly used in menial house-cleaning tasks, or in the entertainment industry. This is the kind of machine that people always imagine when discussing robots, and they are often imagined to have human-like features.
- Field robots: These are robots in charge of military and exploratory tasks. They are b...