Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.

This book will offer ideas on how robots can be used as teachers' assistants to scaffold learning outcomes, where the robot is a learning agent in self-directed learning who can contribute to the development of key competences for today's world through targeted learning - such as engineering thinking, math, physics, computational thinking, etc. starting from pre-school and continuing to a higher education level. Robotization is speeding up at the moment in a variety of dimensions, both through the automation of work, by performing intellectual duties, and by providing support for people in everyday situations. There is increasing political attention, especially in Europe, on educational systems not being able to keep up with such emerging technologies, and efforts to rectify this. This edited volume responds to this attention, and seeks to explore which pedagogical and educational concepts should be included in the learning process so that the use of robots is meaningful from the point of view of knowledge construction, and so that it is safe from the technological and cybersecurity perspective.

• The book provides suitable foundations for instructors and students who are engaging with educational robotics in any discipline, such as such as education, computer science, engineering, philosophy, and psychology. • The authors integrate relevant theories of learning and developmental psychology, such as behaviourism, constructivism, and cognitivism, before discussing the roles that robots play in learning. • Each chapter includes real-world illustrative examples, open-ended reflective questions, and lists of further reading and other resources.

The field of robotics in a classroom context has seen an increase in global momentum recently because of its positive contributions in the teaching of science, technology, engineering, mathematics (STEM) and beyond. It is argued that when robotics and programming are integrated in developmentally appropriate ways, cognitive skill development beyond STEM can be achieved. The development of educational robotics has presented a plethora of ways in which students can be assisted in the classroom. Designing, Constructing, and Programming Robots for Learning highlights the importance of integrating robotics in educational practice and presents various ways for how it can be achieved. It further explains how 21st century skills and life skills can be developed through the hands-on experience of educational robotics. Covering topics such as computational thinking, social skill enhancement, and teacher training, this text is an essential resource for engineers, educational software developers, teachers, professors, instructors, researchers, faculty, leaders in educational fields, students, and academicians.

"This book explores the theory and practice of educational robotics in the K-12 formal and informal educational settings, providing empirical research supporting the use of robotics for STEM learning"--Provided by publisher.

This work brings together the insights of ten designers, researchers, and educators, each invited to contribute a chapter that relates his or her experience develping or using a children's robotic learning device. This growing area of endeavour is expected to have prodound and long-lasting effets on the ways children learn and develop, and its participants come from a wide range of backgrounds.

Contains examples of how robotics can be used in grades K through 2 as a hands-on tool for helping children learn about science, technology, engineering, and mathematics.

A fresh look at a “robot-proof” education in the new age of generative AI. In 2017, Robot-Proof, the first edition, foresaw the advent of the AI economy and called for a new model of higher education designed to help human beings flourish alongside smart machines. That economy has arrived. Creative tasks that, seven years ago, seemed resistant to automation can now be performed with a simple prompt. As a result, we must now learn not only to be conversant with these technologies, but also to comprehend and deploy their outputs. In this revised and updated edition, Joseph Aoun rethinks the university’s mission for a world transformed by AI, advocating for the lifelong endeavor of a “robot-proof” education. Aoun puts forth a framework for a new curriculum, humanics, which integrates technological, data, and human literacies in an experiential setting, and he renews the call for universities to embrace lifelong learning through a social compact with government, employers, and learners themselves. Drawing on the latest developments and debates around generative AI, Robot-Proof is a blueprint for the university as a force for human reinvention in an era of technological change—an era in which we must constantly renegotiate the shifting boundaries between artificial intelligence and the capacities that remain uniquely human.

How might digital technology and notably smart technologies based on artificial intelligence (AI), learning analytics, robotics, and others transform education? This book explores such question. It focuses on how smart technologies currently change education in the classroom and the management of educational organisations and systems.

Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.