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Author: Sonia Dechter
Publisher: Springer Nature
ISBN: 3031015703
Category : Computers
Languages : en
Pages : 109
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Book Description
Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.
Author: Sonia Dechter
Publisher: Springer Nature
ISBN: 3031015703
Category : Computers
Languages : en
Pages : 109
Get Book
Book Description
Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.
Author: Neil Selwyn
Publisher: John Wiley & Sons
ISBN: 1509528989
Category : Social Science
Languages : en
Pages : 114
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Book Description
Developments in AI, robotics and big data are changing the nature of education. Yet the implications of these technologies for the teaching profession are uncertain. While most educators remain convinced of the need for human teachers, outside the profession there is growing anticipation of a technological reinvention of the ways in which teaching and learning take place. Through an examination of technological developments such as autonomous classroom robots, intelligent tutoring systems, learning analytics and automated decision-making, Neil Selwyn highlights the need for nuanced discussions around the capacity of AI to replicate the social, emotional and cognitive qualities of human teachers. He pushes conversations about AI and education into the realm of values, judgements and politics, ultimately arguing that the integration of any technology into society must be presented as a choice. Should Robots Replace Teachers? is a must-read for anyone interested in the future of education and work in our increasingly automated times.
Author: Fady Alnajjar
Publisher: Routledge
ISBN: 1000388840
Category : Education
Languages : en
Pages : 238
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Book Description
Robots in Education is an accessible introduction to the use of robotics in formal learning, encompassing pedagogical and psychological theories as well as implementation in curricula. Today, a variety of communities across education are increasingly using robots as general classroom tutors, tools in STEM projects, and subjects of study. This volume explores how the unique physical and social-interactive capabilities of educational robots can generate bonds with students while freeing instructors to focus on their individualized approaches to teaching and learning. Authored by a uniquely interdisciplinary team of scholars, the book covers the basics of robotics and their supporting technologies; attitudes toward and ethical implications of robots in learning; research methods relevant to extending our knowledge of the field; and more.
Author: Agnese Augello
Publisher: Frontiers Media SA
ISBN: 2889663256
Category : Technology & Engineering
Languages : en
Pages : 167
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Book Description
Author: Allison Druin
Publisher: Morgan Kaufmann
ISBN: 9781558605978
Category : Computers
Languages : en
Pages : 412
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Book Description
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.
Author: Chenguang Yang
Publisher:
ISBN: 9781003119173
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task. This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user's arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.
Author: Luka Peternel
Publisher: Frontiers Media SA
ISBN: 2889633128
Category :
Languages : en
Pages : 229
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Book Description
In the past years there has been considerable effort to move robots from industrial environments to our daily lives where they can collaborate and interact with humans to improve our life quality. One of the key challenges in this direction is to make a suitable robot control system that can adapt to humans and interactively learn from humans to facilitate the efficient and safe co-existence of the two. The applications of such robotic systems include: service robotics and physical human-robot collaboration, assistive and rehabilitation robotics, semi-autonomous cars, etc. To achieve the goal of integrating robotic systems into these applications, several important research directions must be explored. One such direction is the study of skill transfer, where a human operator’s skilled executions are used to obtain an autonomous controller. Another important direction is shared control, where a robotic controller and humans control the same body, tool, mechanism, car, etc. Shared control, in turn invokes very rich research questions such as co-adaptation between the human and the robot, where the two agents can benefit from each other’s skills or must adapt to each other’s behavior to achieve effective cooperative task executions. The aim of this Research Topic is to help bridge the gap between the state-of-the-art and above-mentioned goals through novel multidisciplinary approaches in human-in-the-loop robot control and learning.
Author: Johnny Ch LOK
Publisher:
ISBN:
Category :
Languages : en
Pages : 129
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Book Description
Chapter EightArtificia intelligent future education market development8.1Artificial intelligent robots inventionnegative impactsIndeed, artificial intelligence, computing can learn to something that effectively reasons, thinks, if (AI) learns more powerful and valuable complement to human capabilities; improving medical diaguoses, weather prediction, supply-chain management, transportation and even personal choices about where to go on vacation or what to buy, how to learn teaching any subjects knowledge to assist school teachers to teach students, e.g. accounting, law, architecture, language etc. subjects knowledge It can bring benefits to human it (AI) robots can learn teacher's any subjects to teach students or skillful knowledge, e.g. driving, taking care old people at home, manufacturing vehicles in factories. Otherwise, if (AI) robots learn how to be applied to be weapons to attack enemy. It will bring harm to human's life safety. As a result, technology can assist human's development when it can be applied to do meaning jobs, but it can also damage human's development when it can be applied to do harmful human's behaviour, e.g. attacking enemy to cause robot technological war. Consequently, the negactive impact will be depending on how humans teach (AI) robots to learn when some humans intends to teach (AI) robots to attack enemy. Thus, scientists need to know to educate (AI) robots to learn positive knowledge to attribute to us to raise our standard of living, if it is a tool in the service of humans, making our lives better. Otherwise, who ought not educate (AI) robots to learn negative knowledge to attribute to influence our's life safety when they are applied to attack enemies.However, some scientists believe (AI) robots will have negative impact to influence our economy and society. Such as (AI) robots will destroy most jobs, it will make humans foolish, due to humans depend on (AI) robots to assist us to do any jobs; it will destroy people's privacy and it will enable bias and abuse and it will eventally exterminate humanity. Hence, when time comes to develop computers really think, intelligence is the same things as consciousness, the brain is a computer. Then, scientists must have responsibilities to concern how to teach (AI) robots to learn useful or attributable human's knowledge, not harmful human's knowledge to avoid future invented (AI) robots are taught to learn how to attack ourselves.8.2Future AI tutoring system potentialdevelopment marketHumans can learn (AI) robots how to educate our next generation after it has be taught any knowledge from human. Thus, it brings this question: How can human teach new knowledge to (AI) robots to learn successfully? I shall indicate some scientists' evidences to explain that it is possible (AI) robots had abilities to learn human's mind, judgement and analytical abilities, reading, writing, speaking abilities in future one day absolutely. I shall indicate what is intelligent tutoring systems (ITS) example as below:When are computer-based tutors which act as a supplement to human teachers. The major advantage of an (ITS) is, it can provide personalized instructions to students according to their cognitive abilities. In this scenario, an intelligent tutoring system can be quite relevant to solve unavailability of skilled teachers challenges. Such as India has many students who demend teachers to learn to them, but India is facing skilled teachers shortage challenge. (ITS) are computer-based tutors, which act as a supplement to human teachers. An intelligent tutoring system is educational software containing an artificial intelligence component. The software tracks students work, tailoring feedback and hints along the way. By contenting information on a particular student's performance, the software can make interences about strengths and weaknesses, and can suggest addition work.
Author: Aleksandar Vakanski
Publisher: John Wiley & Sons
ISBN: 1119091780
Category : Technology & Engineering
Languages : en
Pages : 208
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Book Description
This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstraction Discusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problem Focuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regression Concentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert
Author: Jianwei Zhang
Publisher: Frontiers Media SA
ISBN: 2889762548
Category : Science
Languages : en
Pages : 295
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Book Description
The purpose of this Research Topic is to reflect and discuss links between neuroscience, psychology, computer science and robotics with regards to the topic of cross-modal learning which has, in recent years, emerged as a new area of interdisciplinary research. The term cross-modal learning refers to the synergistic synthesis of information from multiple sensory modalities such that the learning that occurs within any individual sensory modality can be enhanced with information from one or more other modalities. Cross-modal learning is a crucial component of adaptive behavior in a continuously changing world, and examples are ubiquitous, such as: learning to grasp and manipulate objects; learning to walk; learning to read and write; learning to understand language and its referents; etc. In all these examples, visual, auditory, somatosensory or other modalities have to be integrated, and learning must be cross-modal. In fact, the broad range of acquired human skills are cross-modal, and many of the most advanced human capabilities, such as those involved in social cognition, require learning from the richest combinations of cross-modal information. In contrast, even the very best systems in Artificial Intelligence (AI) and robotics have taken only tiny steps in this direction. Building a system that composes a global perspective from multiple distinct sources, types of data, and sensory modalities is a grand challenge of AI, yet it is specific enough that it can be studied quite rigorously and in such detail that the prospect for deep insights into these mechanisms is quite plausible in the near term. Cross-modal learning is a broad, interdisciplinary topic that has not yet coalesced into a single, unified field. Instead, there are many separate fields, each tackling the concerns of cross-modal learning from its own perspective, with currently little overlap. We anticipate an accelerating trend towards integration of these areas and we intend to contribute to that integration. By focusing on cross-modal learning, the proposed Research Topic can bring together recent progress in artificial intelligence, robotics, psychology and neuroscience.
