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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.
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.
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.
In Learn Robotics with Raspberry Pi, you'll learn how to build and code your own robot projects with just the Raspberry Pi microcomputer and a few easy-to-get components - no prior experience necessary! Learn Robotics with Raspberry Pi will take you from inexperienced maker to robot builder. You'll start off building a two-wheeled robot powered by a Raspberry Pi minicomputer and then program it using Python, the world's most popular programming language. Gradually, you'll improve your robot by adding increasingly advanced functionality until it can follow lines, avoid obstacles, and even recognize objects of a certain size and color using computer vision. Learn how to: - Control your robot remotely using only a Wii remote - Teach your robot to use sensors to avoid obstacles - Program your robot to follow a line autonomously - Customize your robot with LEDs and speakers to make it light up and play sounds - See what your robot sees with a Pi Camera As you work through the book, you'll learn fundamental electronics skills like how to wire up parts, use resistors and regulators, and determine how much power your robot needs. By the end, you'll have learned the basics of coding in Python and know enough about working with hardware like LEDs, motors, and sensors to expand your creations beyond simple robots.
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.
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.
Robot learning is a broad and interdisciplinary area. This holds with regard to the basic interests and the scienti c background of the researchers involved, as well as with regard to the techniques and approaches used. The interests that motivate the researchers in this eld range from fundamental research issues, such as how to constructively understand intelligence, to purely application o- ented work, such as the exploitation of learning techniques for industrial robotics. Given this broad scope of interests, it is not surprising that, although AI and robotics are usually the core of the robot learning eld, disciplines like cog- tive science, mathematics, social sciences, neuroscience, biology, and electrical engineering have also begun to play a role in it. In this way, its interdisciplinary character is more than a mere fashion, and leads to a productive exchange of ideas. One of the aims of EWLR-6 was to foster this exchange of ideas and to f- ther boost contacts between the di erent scienti c areas involved in learning robots. EWLR is, traditionally, a \European Workshop on Learning Robots". Nevertheless, the organizers of EWLR-6 decided to open up the workshop to non-European research as well, and included in the program committee we- known non-European researchers. This strategy proved to be successful since there was a strong participation in the workshop from researchers outside - rope, especially from Japan, which provided new ideas and lead to new contacts.
• 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 truth about robots: two experts look beyond the hype, offering a lively and accessible guide to what robots can (and can't) do. There’s a lot of hype about robots; some of it is scary and some of it utopian. In this accessible book, two robotics experts reveal the truth about what robots can and can’t do, how they work, and what we can reasonably expect their future capabilities to be. It will not only make you think differently about the capabilities of robots; it will make you think differently about the capabilities of humans. Ruth Aylett and Patricia Vargas discuss the history of our fascination with robots—from chatbots and prosthetics to autonomous cars and robot swarms. They show us the ways in which robots outperform humans and the ways they fall woefully short of our superior talents. They explain how robots see, feel, hear, think, and learn; describe how robots can cooperate; and consider robots as pets, butlers, and companions. Finally, they look at robots that raise ethical and social issues: killer robots, sexbots, and robots that might be gunning for your job. Living with Robots equips readers to look at robots concretely—as human-made artifacts rather than placeholders for our anxieties. Find out: •Why robots can swim and fly but find it difficult to walk •Which robot features are inspired by animals and insects •Why we develop feelings for robots •Which human abilities are hard for robots to emulate
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis