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This volume gathers together cutting-edge research from the Third Workshop on Algorithmic Foundations of Robotics and gives a solid overview of the state of the art in robot algorithms. The papers cover core problems in robotics, such as motion planning, sensor-based planning, manipulation, and assembly planning. They also examine the application o
Familiarizes machine learning experts with imitation learning, statistical supervised learning theory, and reinforcement learning. It also roboticists and experts in applied artificial intelligence with a broader appreciation for the frameworks and tools available for imitation learning.
Selected contributions to the Workshop WAFR 2002, held December 15-17, 2002, Nice, France. This fifth biannual Workshop on Algorithmic Foundations of Robotics focuses on algorithmic issues related to robotics and automation. The design and analysis of robot algorithms raises fundamental questions in computer science, computational geometry, mechanical modeling, operations research, control theory, and associated fields. The highly selective program highlights significant new results such as algorithmic models and complexity bounds. The validation of algorithms, design concepts, or techniques is the common thread running through this focused collection.
This book gathers the outcomes of the thirteenth Workshop on the Algorithmic Foundations of Robotics (WAFR), the premier event for showcasing cutting-edge research on algorithmic robotics. The latest WAFR, held at Universidad Politécnica de Yucatán in Mérida, México on December 9–11, 2018, continued this tradition. This book contains fifty-four papers presented at WAFR, which highlight the latest research on fundamental algorithmic robotics (e.g., planning, learning, navigation, control, manipulation, optimality, completeness, and complexity) demonstrated through several applications involving multi-robot systems, perception, and contact manipulation. Addressing a diverse range of topics in papers prepared by expert contributors, the book reflects the state of the art and outlines future directions in the field of algorithmic robotics.
“Essential reading for all who have a vested interest in the rise of AI.” —Daryl Li, AI & Society “Thought-provoking...Explores how we can best try to ensure that robots work for us, rather than against us, and proposes a new set of laws to provide a conceptual framework for our thinking on the subject.” —Financial Times “Pasquale calls for a society-wide reengineering of policy, politics, economics, and labor relations to set technology on a more regulated and egalitarian path...Makes a good case for injecting more bureaucracy into our techno-dreams, if we really want to make the world a better place.” —Wired “Pasquale is one of the leading voices on the uneven and often unfair consequences of AI in our society...Every policymaker should read this book and seek his counsel.” —Safiya Noble, author of Algorithms of Oppression Too many CEOs tell a simple story about the future of work: if a machine can do what you do, your job will be automated, and you will be replaced. They envision everyone from doctors to soldiers rendered superfluous by ever-more-powerful AI. Another story is possible. In virtually every walk of life, robotic systems can make labor more valuable, not less. Frank Pasquale tells the story of nurses, teachers, designers, and others who partner with technologists, rather than meekly serving as data sources for their computerized replacements. This cooperation reveals the kind of technological advance that could bring us all better health care, education, and more, while maintaining meaningful work. These partnerships also show how law and regulation can promote prosperity for all, rather than a zero-sum race of humans against machines. Policymakers must not allow corporations or engineers alone to answer questions about how far AI should be entrusted to assume tasks once performed by humans, or about the optimal mix of robotic and human interaction. The kind of automation we get—and who benefits from it—will depend on myriad small decisions about how to develop AI. Pasquale proposes ways to democratize that decision-making, rather than centralize it in unaccountable firms. Sober yet optimistic, New Laws of Robotics offers an inspiring vision of technological progress, in which human capacities and expertise are the irreplaceable center of an inclusive economy.
An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.
This book introduces concepts in mobile, autonomous robotics to 3rd-4th year students in Computer Science or a related discipline. The book covers principles of robot motion, forward and inverse kinematics of robotic arms and simple wheeled platforms, perception, error propagation, localization and simultaneous localization and mapping. The cover picture shows a wind-up toy that is smart enough to not fall off a table just using intelligent mechanism design and illustrate the importance of the mechanism in designing intelligent, autonomous systems. This book is open source, open to contributions, and released under a creative common license.
Intersectional Automations explores a range of situations where robotics, biotechnological enhancement, artificial intelligence (AI), and algorithmic culture collide with intersectional social justice issues such as race, class, gender, sexuality, ability, and citizenship. As robots, machine learning applications, and human augmentics are artifacts of human culture, they sometimes carry stereotypes, biases, exclusions, and other forms of privilege into their computational logics, platforms, and/or embodiments. The essays in this multidisciplinary collection consider how questions of equity and social justice impact our understanding of these developments, analyzing not only the artifacts themselves, but also the discourses and practices surrounding them, including societal understandings, design choices, law and policy approaches, and their uses and abuses.
This textbook offers a tutorial introduction to robotics and Computer Vision which is light and easy to absorb. The practice of robotic vision involves the application of computational algorithms to data. Over the fairly recent history of the fields of robotics and computer vision a very large body of algorithms has been developed. However this body of knowledge is something of a barrier for anybody entering the field, or even looking to see if they want to enter the field — What is the right algorithm for a particular problem?, and importantly: How can I try it out without spending days coding and debugging it from the original research papers? The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used —instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals light and color, camera modelling, image processing, feature extraction and multi-view geometry, and bring it all together in a visual servo system. “An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished Oussama Khatib, Stanford
Algorithms, the heart of robotics, form the connection between data collected by sensors and the robotís activities. They also serve as a medium to describe the foundations and principles of robotics.Paper Topics Include: Motion Planning * Navigation * Manipulation * Grasping * Assembly * Controllability * Recognizability * Learning and Distributed Control * Task-Specific Manipulator Design * Simulation of Linkages and Collisions * Completeness and Complexity Measures * Computational Algebra and Geometry