Robot Learning
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Robot Learning
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2024-12-18
Robot Learning written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-18 with Technology & Engineering categories.
Robot learning-This chapter introduces the concept of robot learning, explaining how robots can autonomously acquire knowledge from their environment to improve their performance and decisionmaking Domo (robot)-The Domo robot is explored as a case study in the evolution of robot learning, with insights into its learning methods and its ability to adapt through sensory feedback Developmental robotics-This chapter covers the fundamentals of developmental robotics, focusing on how robots can learn progressively over time, similar to human cognitive development ICub-A deep dive into the iCub robot, emphasizing its role in studying cognitive development and humanrobot interaction, showcasing its advanced learning capabilities Programming by demonstration-Discussing how robots can be programmed through demonstrations by human operators, this chapter highlights the ease and efficiency of teaching robots complex tasks Neurorobotics-Neurorobotics blends neuroscience with robotics, and this chapter examines how robot learning is influenced by understanding the brain's processes and how they can be replicated in machines Daniela Rus: Focused on the work of Daniela Rus, a leading researcher in the field of robotics, this chapter examines her contributions to robot learning and autonomous systems Situated approach (artificial intelligence)-A look at the situated approach in AI, where robots learn by interacting directly with their environments, emphasizing the importance of realworld context in robot learning Google Brain: This chapter explores the intersection of deep learning and robotics, specifically the impact of Google Brain's research on enhancing robot learning through advanced algorithms and neural networks James J. Kuffner Jr.-An analysis of James J. Kuffner's pioneering work in robotics and his contributions to motion planning and robot learning techniques that allow robots to perform complex tasks Cloud robotics: Cloud robotics is reshaping the way robots learn by leveraging cloud computing to process and store large amounts of data. This chapter outlines how this innovation impacts robot learning and its scalability JeanChristophe Baillie-Focusing on the work of JeanChristophe Baillie, this chapter delves into his exploration of robot learning from a systems perspective, particularly in mobile robotics and sensory processing Stephen E. Levinson-This chapter examines Stephen E. Levinson's contributions to robot learning, particularly his work in integrating robotics with natural language processing and cognitive science Ashutosh Saxena-Ashutosh Saxena's work in creating robots that learn from human actions is discussed in this chapter, highlighting how robots can be trained to understand and replicate human behavior Aude Billard-Aude Billard's research in humanrobot interaction is covered here, focusing on the development of robots that can learn from social cues and human collaboration Vivian Chu-Vivian Chu's work on robot learning, particularly in the context of robotic arm movements and realtime learning through feedback, is explored in this chapter Juyang Weng-This chapter covers Juyang Weng's approach to embodied cognition in robotics, highlighting how robots can learn from their own experiences in the physical world Andy Zeng-Andy Zeng's contributions to deep reinforcement learning in robotics are explored, focusing on how robots can adapt and learn complex behaviors autonomously Android (robot)-The Android robot, known for its humanlike appearance and learning capabilities, is examined, offering insights into how robots can be designed to closely replicate human intelligence Humanoid robot-Humanoid robots, their design, and their learning algorithms are discussed in this chapter, focusing on their role in improving humanrobot interaction and learning capabilities
Robot Learning
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Author : Suraiya Jabin
language : en
Publisher: BoD – Books on Demand
Release Date : 2010-08-12
Robot Learning written by Suraiya Jabin and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-08-12 with Computers categories.
Robot Learning is intended for one term advanced Machine Learning courses taken by students from different computer science research disciplines. This text has all the features of a renowned best selling text. It gives a focused introduction to the primary themes in a Robot learning course and demonstrates the relevance and practicality of various Machine Learning algorithms to a wide variety of real-world applications from evolutionary techniques to reinforcement learning, classification, control, uncertainty and many other important fields. Salient features: - Comprehensive coverage of Evolutionary Techniques, Reinforcement Learning and Uncertainty. - Precise mathematical language used without excessive formalism and abstraction. - Included applications demonstrate the utility of the subject in terms of real-world problems. - A separate chapter on Anticipatory-mechanisms-of-human-sensory-motor-coordination and biped locomotion. - Collection of most recent research on Robot Learning.
Robot Learning
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Author : J. H. Connell
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Robot Learning written by J. H. Connell and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
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.
Learning For Adaptive And Reactive Robot Control
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Author : Aude Billard
language : en
Publisher: MIT Press
Release Date : 2022-02-08
Learning For Adaptive And Reactive Robot Control written by Aude Billard and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-08 with Technology & Engineering categories.
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.
Ai Based Robot Safe Learning And Control
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Author : Xuefeng Zhou
language : en
Publisher: Springer Nature
Release Date : 2020-06-02
Ai Based Robot Safe Learning And Control written by Xuefeng Zhou and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-02 with Technology & Engineering categories.
This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.
Recent Advances In Robot Learning
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Author : Judy A. Franklin
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Recent Advances In Robot Learning written by Judy A. Franklin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).
Advances In Robot Learning
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Author : Jeremy Wyatt
language : en
Publisher: Springer
Release Date : 2003-06-29
Advances In Robot Learning written by Jeremy Wyatt and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-29 with Computers categories.
This book constitutes the thoroughly refereed post-workshop proceedings of the 8th European Workshop on Learning Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.The seven revised full workshop papers presented were carefully reviewed and selected for inclusion in the book. Also included are two invited full papers. Among the topics addressed are map building for robot navigation, multi-task reinforcement learning, neural network approaches, example-based learning, situated agents, planning maps for mobile robots, path finding, autonomous robots, and biologically inspired approaches.
Robot Learning From Human Teachers
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Author : Sonia Chernova
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Robot Learning From Human Teachers written by Sonia Chernova and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
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.
Robot Learning By Visual Observation
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Author : Aleksandar Vakanski
language : en
Publisher: John Wiley & Sons
Release Date : 2017-01-13
Robot Learning By Visual Observation written by Aleksandar Vakanski and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-13 with Technology & Engineering categories.
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
Interdisciplinary Approaches To Robot Learning
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Author : Andreas Birk
language : en
Publisher: World Scientific
Release Date : 2000-06-12
Interdisciplinary Approaches To Robot Learning written by Andreas Birk and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-06-12 with Technology & Engineering categories.
Robots are being used in increasingly complicated and demanding tasks, often in environments that are complex or even hostile. Underwater, space and volcano exploration are just some of the activities that robots are taking part in, mainly because the environments that are being explored are dangerous for humans. Robots can also inhabit dynamic environments, for example to operate among humans, not just in factories, but also taking on more active roles. Recently, for instance, they have made their way into the home entertainment market. Given the variety of situations that robots will be placed in, learning becomes increasingly important.Robot learning is essentially about equipping robots with the capacity to improve their behaviour over time, based on their incoming experiences. The papers in this volume present a variety of techniques. Each paper provides a mini-introduction to a subfield of robot learning. Some also give a fine introduction to the field of robot learning as a whole. There is one unifying aspect to the work reported in the book, namely its interdisciplinary nature, especially in the combination of robotics, computer science and biology. This approach has two important benefits: first, the study of learning in biological systems can provide robot learning scientists and engineers with valuable insights into learning mechanisms of proven functionality and versatility; second, computational models of learning in biological systems, and their implementation in simulated agents and robots, can provide researchers of biological systems with a powerful platform for the development and testing of learning theories.