Название: Human-Robot Interaction: An Introduction, 2nd Edition
Автор: Christoph Bartneck, Tony Belpaeme, Friederike Eyssel, Takayuki Kanda
Издательство: Cambridge University Press
Год: 2024
Страниц: 323
Язык: английский
Формат: pdf (true)
Размер: 24.0 MB
The role of robots in society keeps expanding and diversifying, bringing with it a host of issues surrounding the relationship between robots and humans. This introduction to human–robot interaction (HRI) by leading researchers in this developing field is the first to provide a broad overview of the multidisciplinary topics central to modern HRI research. Written for students and researchers from robotics, Artificial Intelligence (AI), psychology, sociology, and design, it presents the basics of how robots work, how to design them, and how to evaluate their performance. Self-contained chapters discuss a wide range of topics, including speech and language, nonverbal communication, and processing emotions, plus an array of applications and the ethical issues surrounding them. This revised and expanded second edition includes a new chapter on how people perceive robots, coverage of recent developments in robotic hardware, software, and Artificial Intelligence, and exercises for readers to test their knowledge. Middleware is software that sits among software components, such as commonly available library modules and the application modules that the developers created for a specific purpose, as well as the operating system of the robot’s computer. It is often considered as the “software glue” because its function is to ease the connection of those software components. The Robot Operating System (ROS) is a middleware platform commonly used in the robotics and HRI community. Computer Vision is an important area for HRI. Another computer-vision technique relevant to HRI is the processing of faces. The ability to detect faces in an image has advanced and can be used, for example, to let the robot look people in the eye. Face recognition is still a challenge, however. Impressive progress has been made in the last decade, mainly fueled by the evolution of Deep Learning, and it is now possible to reliably recognize and distinguish between hundreds of people when they are facing the camera.
