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Author: Walter Van de Velde
Publisher: MIT Press
ISBN: 9780262720175
Category : Computers
Languages : en
Pages : 182
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Book Description
The contributions in Toward Learning Robots address the question of how a robot can be designed to acquire autonomously whatever it needs to realize adequate behavior in a complex environment. In-depth discussions of issues, techniques, and experiments in machine learning focus on improving ease of programming and enhancing robustness in unpredictable and changing environments, given limitations of time and resources available to researchers. The authors show practical progress toward a useful set of abstractions and techniques to describe and automate various aspects of learning in autonomous systems. The close interaction of such a system with the world reveals opportunities for new architectures and learning scenarios and for grounding symbolic representations, though such thorny problems as noise, choice of language, abstraction level of representation, and operationality have to be faced head-on. Contents Introduction: Toward Learning Robots * Learning Reliable Manipulation Strategies without Initial Physical Models * Learning by an Autonomous Agent in the Pushing Domain * A Cost-Sensitive Machine Learning Method for the Approach and Recognize Task * A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations * Understanding Object Motion: Recognition, Learning and Spatiotemporal Reasoning * Learning How to Plan * Robo-Soar: An Integration of External Interaction, Planning, and Learning Using Soar * Foundations of Learning in Autonomous Agents * Prior Knowledge and Autonomous Learning
Author: Walter Van de Velde
Publisher: MIT Press
ISBN: 9780262720175
Category : Computers
Languages : en
Pages : 182
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Book Description
The contributions in Toward Learning Robots address the question of how a robot can be designed to acquire autonomously whatever it needs to realize adequate behavior in a complex environment. In-depth discussions of issues, techniques, and experiments in machine learning focus on improving ease of programming and enhancing robustness in unpredictable and changing environments, given limitations of time and resources available to researchers. The authors show practical progress toward a useful set of abstractions and techniques to describe and automate various aspects of learning in autonomous systems. The close interaction of such a system with the world reveals opportunities for new architectures and learning scenarios and for grounding symbolic representations, though such thorny problems as noise, choice of language, abstraction level of representation, and operationality have to be faced head-on. Contents Introduction: Toward Learning Robots * Learning Reliable Manipulation Strategies without Initial Physical Models * Learning by an Autonomous Agent in the Pushing Domain * A Cost-Sensitive Machine Learning Method for the Approach and Recognize Task * A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations * Understanding Object Motion: Recognition, Learning and Spatiotemporal Reasoning * Learning How to Plan * Robo-Soar: An Integration of External Interaction, Planning, and Learning Using Soar * Foundations of Learning in Autonomous Agents * Prior Knowledge and Autonomous Learning
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1668424126
Category : Education
Languages : en
Pages : 969
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Book Description
The education system is constantly growing and developing as more ways to teach and learn are implemented into the classroom. Recently, there has been a growing interest in teaching computational thinking with schools all over the world introducing it to the curriculum due to its ability to allow students to become proficient at problem solving using logic, an essential life skill. In order to provide the best education possible, it is imperative that computational thinking strategies, along with programming skills and the use of robotics in the classroom, be implemented in order for students to achieve maximum thought processing skills and computer competencies. The Research Anthology on Computational Thinking, Programming, and Robotics in the Classroom is an all-encompassing reference book that discusses how computational thinking, programming, and robotics can be used in education as well as the benefits and difficulties of implementing these elements into the classroom. The book includes strategies for preparing educators to teach computational thinking in the classroom as well as design techniques for incorporating these practices into various levels of school curriculum and within a variety of subjects. Covering topics ranging from decomposition to robot learning, this book is ideal for educators, computer scientists, administrators, academicians, students, and anyone interested in learning more about how computational thinking, programming, and robotics can change the current education system.
Author: J. H. Connell
Publisher: Springer Science & Business Media
ISBN: 1461531845
Category : Technology & Engineering
Languages : en
Pages : 247
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Book Description
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.
Author: Linda Daniela
Publisher: Springer
ISBN: 3030199134
Category : Education
Languages : en
Pages : 368
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Book Description
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.
Author: Fady Alnajjar
Publisher: Taylor & Francis
ISBN: 1000388859
Category : Education
Languages : en
Pages : 204
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Book Description
• 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.
Author: Yasser Mohammad
Publisher: Springer
ISBN: 3319252321
Category : Computers
Languages : en
Pages : 330
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Book Description
This book explores an approach to social robotics based solely on autonomous unsupervised techniques and positions it within a structured exposition of related research in psychology, neuroscience, HRI, and data mining. The authors present an autonomous and developmental approach that allows the robot to learn interactive behavior by imitating humans using algorithms from time-series analysis and machine learning. The first part provides a comprehensive and structured introduction to time-series analysis, change point discovery, motif discovery and causality analysis focusing on possible applicability to HRI problems. Detailed explanations of all the algorithms involved are provided with open-source implementations in MATLAB enabling the reader to experiment with them. Imitation and simulation are the key technologies used to attain social behavior autonomously in the proposed approach. Part two gives the reader a wide overview of research in these areas in psychology, and ethology. Based on this background, the authors discuss approaches to endow robots with the ability to autonomously learn how to be social. Data Mining for Social Robots will be essential reading for graduate students and practitioners interested in social and developmental robotics.
Author: Alexandros Iosifidis
Publisher: Academic Press
ISBN: 0323885721
Category : Technology & Engineering
Languages : en
Pages : 638
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Book Description
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
Author: David Scaradozzi
Publisher: Springer Nature
ISBN: 3030770400
Category : Technology & Engineering
Languages : en
Pages : 364
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Book Description
This open access book contains observations, outlines, and analyses of educational robotics methodologies and activities, and developments in the field of educational robotics emerging from the findings presented at FabLearn Italy 2019, the international conference that brought together researchers, teachers, educators and practitioners to discuss the principles of Making and educational robotics in formal, non-formal and informal education. The editors’ analysis of these extended versions of papers presented at FabLearn Italy 2019 highlight the latest findings on learning models based on Making and educational robotics. The authors investigate how innovative educational tools and methodologies can support a novel, more effective and more inclusive learner-centered approach to education. The following key topics are the focus of discussion: Makerspaces and Fab Labs in schools, a maker approach to teaching and learning; laboratory teaching and the maker approach, models, methods and instruments; curricular and non-curricular robotics in formal, non-formal and informal education; social and assistive robotics in education; the effect of innovative spaces and learning environments on the innovation of teaching, good practices and pilot projects.
Author: Aude Billard
Publisher: MIT Press
ISBN: 0262367017
Category : Technology & Engineering
Languages : en
Pages : 425
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Book Description
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.
Author: Getachew Hailu
Publisher: Peter Lang Publishing
ISBN:
Category : Computers
Languages : en
Pages : 180
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Book Description
Reinforcement learning, in a nutshell, is a form of learning that enables the robot to construct a control law by a system of feedback signals that reinforce «electrical path ways» that produce correct response, and conversely wipe-out connections that produce errors. Unfortunately, without biasing, it is a weak learning that presents unreasonable difficulty, especially when it is applied to real robots. The subject of this thesis is to study, for a particular class of problems, the effects of different form of biases on the speed of learning as well as on the quality of final learned policy, and to realize this learning paradigm on a physical robot by appropriately biasing the robot with domain knowledge that determines how much the robot knows about the different parts of its world.
