Download or read book Learning for Adaptive and Reactive Robot Control written by Aude Billard and published by MIT Press. This book was released on 2022-02-08 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Machine Learning for Robotics Applications written by Monica Bianchini and published by Springer Nature. This book was released on 2021-04-23 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has become one of the most prevalent topics in recent years. The application of machine learning we see today is a tip of the iceberg. The machine learning revolution has just started to roll out. It is becoming an integral part of all modern electronic devices. Applications in automation areas like automotive, security and surveillance, augmented reality, smart home, retail automation and healthcare are few of them. Robotics is also rising to dominate the automated world. The future applications of machine learning in the robotics area are still undiscovered to the common readers. We are, therefore, putting an effort to write this edited book on the future applications of machine learning on robotics where several applications have been included in separate chapters. The content of the book is technical. It has been tried to cover all possible application areas of Robotics using machine learning. This book will provide the future vision on the unexplored areas of applications of Robotics using machine learning. The ideas to be presented in this book are backed up by original research results. The chapter provided here in-depth look with all necessary theory and mathematical calculations. It will be perfect for laymen and developers as it will combine both advanced and introductory material to form an argument for what machine learning could achieve in the future. It will provide a vision on future areas of application and their approach in detail. Therefore, this book will be immensely beneficial for the academicians, researchers and industry project managers to develop their new project and thereby beneficial for mankind. Original research and review works with model and build Robotics applications using Machine learning are included as chapters in this book.
Download or read book Machine Learning Applications to Robot Control written by Omar Abdul-hadi and published by . This book was released on 2018 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control of robot manipulators can be greatly improved with the use of velocity and torque feedforward control. However, the effectiveness of feedforward control greatly relies on the accuracy of the model. In this study, kinematics and dynamics analysis is performed on a six axis arm, a Delta2 robot, and a Delta3 robot. Velocity feedforward calculation is performed using the traditional means of using the kinematics solution for velocity. However, a neural network is used to model the torque feedforward equations. For each of these mechanisms, we first solve the forward and inverse kinematics transformations. We then derive a dynamic model. Later, unlike traditional methods of obtaining the dynamics parameters of the dynamics model, the dynamics model is used to infer dependencies between the input and output variables for neural network torque estimation. The neural network is trained with joint positions, velocities, and accelerations as inputs, and joint torques as outputs. After training is complete, the neural network is used to estimate the feedforward torque effort. Additionally, an investigation is done on the use of neural networks for deriving the inverse kinematics solution of a six axis arm. Although the neural network demonstrated outstanding ability to model complex mathematical equations, the inverse kinematics solution was not accurate enough for practical use.
Download or read book Recent Advances in Robot Learning written by Judy A. Franklin and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Download or read book Robotic Vision Technologies for Machine Learning and Vision Applications written by Garcia-Rodriguez, Jose and published by IGI Global. This book was released on 2012-12-31 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic systems consist of object or scene recognition, vision-based motion control, vision-based mapping, and dense range sensing, and are used for identification and navigation. As these computer vision and robotic connections continue to develop, the benefits of vision technology including savings, improved quality, reliability, safety, and productivity are revealed. Robotic Vision: Technologies for Machine Learning and Vision Applications is a comprehensive collection which highlights a solid framework for understanding existing work and planning future research. This book includes current research on the fields of robotics, machine vision, image processing and pattern recognition that is important to applying machine vision methods in the real world.
Download or read book Legged Robots that Balance written by Marc H. Raibert and published by MIT Press. This book was released on 1986 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, by a leading authority on legged locomotion, presents exciting engineering and science, along with fascinating implications for theories of human motor control. It lays fundamental groundwork in legged locomotion, one of the least developed areas of robotics, addressing the possibility of building useful legged robots that run and balance. The book describes the study of physical machines that run and balance on just one leg, including analysis, computer simulation, and laboratory experiments. Contrary to expectations, it reveals that control of such machines is not particularly difficult. It describes how the principles of locomotion discovered with one leg can be extended to systems with several legs and reports preliminary experiments with a quadruped machine that runs using these principles. Raibert's work is unique in its emphasis on dynamics and active balance, aspects of the problem that have played a minor role in most previous work. His studies focus on the central issues of balance and dynamic control, while avoiding several problems that have dominated previous research on legged machines. Marc Raibert is Associate Professor of Computer Science and Robotics at Carnegie-Mellon University and on the editorial board of The MIT Press journal, Robotics Research. Legged Robots That Balanceis fifteenth in the Artificial Intelligence Series, edited by Patrick Winston and Michael Brady.
Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Learning Control written by Dan Zhang and published by Elsevier. This book was released on 2020-12-05 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length. Provides foundational control theory concepts, along with advanced techniques and the latest advances in adaptive control and robotics Introduces state-of-the-art learning-based control technologies and their applications in robotics and other complex dynamical systems Demonstrates computational techniques for control systems Covers iterative learning impedance control in both human-robot interaction and collaborative robots
Download or read book Reinforcement Learning written by Phil Winder Ph.D. and published by "O'Reilly Media, Inc.". This book was released on 2020-11-06 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website
Download or read book Intelligent Control and Applications for Robotics Volume II written by Yimin Zhou and published by Frontiers Media SA. This book was released on 2023-11-22 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driven by sustaining demands from industrial automation, space applications and the lack of labor forces, robotics has received increasing attention from researchers in the field of automation and control. Optimizing control schemes is critical to fully exploit the potential of industrial and daily-use robots. Usually, accuracy and repeatability are measured to evaluate the performance of a robot, and deviation of the two parameters from normal status would inevitably leads to positional error and creates a problem for the process. Moreover, the repeatability of a robot is different in various parts of the working envelope, fluctuating with speed and payload. Due to the inherent complexity, an advanced learning methodology is crucial to the self-learning and fast adaptation to disturbances.
Download or read book AI based Robot Safe Learning and Control written by Xuefeng Zhou and published by Springer Nature. This book was released on 2020-06-02 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Intelligent Control of Robotic Systems written by D. Katic and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: As robotic systems make their way into standard practice, they have opened the door to a wide spectrum of complex applications. Such applications usually demand that the robots be highly intelligent. Future robots are likely to have greater sensory capabilities, more intelligence, higher levels of manual dexter ity, and adequate mobility, compared to humans. In order to ensure high-quality control and performance in robotics, new intelligent control techniques must be developed, which are capable of coping with task complexity, multi-objective decision making, large volumes of perception data and substantial amounts of heuristic information. Hence, the pursuit of intelligent autonomous robotic systems has been a topic of much fascinating research in recent years. On the other hand, as emerging technologies, Soft Computing paradigms consisting of complementary elements of Fuzzy Logic, Neural Computing and Evolutionary Computation are viewed as the most promising methods towards intelligent robotic systems. Due to their strong learning and cognitive ability and good tolerance of uncertainty and imprecision, Soft Computing techniques have found wide application in the area of intelligent control of robotic systems.
Download or read book Building Smart Robots Using ROS written by Robin Tommy and published by BPB Publications. This book was released on 2022-03-24 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: A beginner’s guide to learn ROS, robotics platform, and practice building robotics system KEY FEATURES ● A step-by-step guide covering the robot's design, assembly, navigation and control. ● Numerous techniques, ROS packages, object detection and image processing concepts included. ● Practical exercises and sample codes to robotics design, simulation, and visualization tools. DESCRIPTION This book is a practical introduction to the Robotics operating system (ROS). It will expose you to the essential principles, tools, and packages in ROS and assist you in configuring and recombining components for additional tasks. If you are new to the world of robotics, you will enjoy the companionship of this book as it guides you through the process of building your first robot. The book introduces robotics and advances through numerous concepts such as sensors and actuators, SLAM, Aruco markers, CAD (computer-aided design), React native application development, image processing in ROS, machine learning and object detection. Every point raised above is illustrated in a live robotics environment. Along the way, other packages required for developing ROS apps will be presented, including serial, OpenCV, and cv bridge. You'll learn about tools like SolidWorks, Moveit, Rviz, as well as simulation platforms like gazebo and turtlesim, which will give you a complete picture of what it takes to build a robot. This book presents an in-depth examination of Robot Operating Systems (ROS), the sole foundation for developing robotics applications. The book guides the readers through investigating and embedding machine learning code to introduce intelligence into the robot. WHAT YOU WILL LEARN ● Develop a stronghold on basics of robotics with code samples and illustrations. ● Familiarity with ROS, the configuration of nodes, and 3D robot simulations. ● Learn how to publish data to the ROS network for web integration. ● Learn about SLAM, CAD, React Native, and ROS image processing. ● Learn about Artificial Intelligence principles and object detection with ROS. ● Complete design, simulation, and assembly of a robot. WHO THIS BOOK IS FOR The book is aimed at robotics developers, hardware product designers, full-stack application developers, machine learning enthusiasts, and students who want to obtain real-world experience in robotics development from start to finish. Having some experience with Ubuntu and the python programming language would be helpful. TABLE OF CONTENTS 1. ROS 2. Writing Nodes 3. Sensors and Actuators 4. ROS SERIAL 5. Web interface 6. Turtle Sim Simulation 7. Designing a robot 8. Gazebo 9. Moveit 10. Rviz 11. Vision 12. Aruco Markers 13. SLAM 14. React Native App 15. Artificial Intelligence
Download or read book Towards Affordance Based Robot Control written by Erich Rome and published by Springer Science & Business Media. This book was released on 2008-02-11 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today’s mobile robot perception is insufficient for acting goal-directedly in unconstrained, dynamic everyday environments like a home, a factory, or a city. Subject to restrictions in bandwidth, computer power, and computation time, a robot has to react to a wealth of dynamically changing stimuli in such environments, requiring rapid, selective attention to decisive, action-relevant information of high current utility. Robust and general engineering methods for effectively and efficiently coupling perception, action and reasoning are unavailable. Interesting performance, if any, is currently only achieved by sophisticated robot programming exploiting domain features and specialties, which leaves ordinary users no chance of changing how the robot acts. The purpose of this volume - outcome of a GI-Dagstuhl Seminar held in Dagstuhl Castle in June 2006 - is to give a first overview on the concept of affordances for the design and implementation of autonomous mobile robots acting goal-directedly in a dynamic environment. The aim is to develop affordance-based control as a method for robotics. The potential of this new methodology will be shown by going beyond navigation-like tasks towards goaldirected autonomous manipulation in the project demonstrators.
Download or read book Artificial Intelligence and Robotics written by Huimin Lu and published by Springer Nature. This book was released on 2020-11-10 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides insights into research in the field of artificial intelligence in combination with robotics technologies. The integration of artificial intelligence and robotic technologies is a highly topical area for researchers and developers from academia and industry around the globe, and it is likely that artificial intelligence will become the main approach for the next generation of robotics research. The tremendous number of artificial intelligence algorithms and big data solutions has significantly extended the range of potential applications for robotic technologies, and has also brought new challenges for the artificial intelligence community. Sharing recent advances in the field, the book features papers by young researchers presented at the 4th International Symposium on Artificial Intelligence and Robotics 2019 (ISAIR2019), held in Daegu, Korea, on August 20–24, 2019.
Download or read book Robot Intelligence Technology and Applications 7 written by Jun Jo and published by Springer Nature. This book was released on 2023-02-28 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are starting to enter a post-COVID-19 life. While this pandemic has made everyone’s life challenging, it also expedited the transition of our everyday lives into a new form, often called “The New Normal.” Although many people often use the terminology, perhaps we still do not have consensus about what it is and what it should be like. However, one thing that is clear namely that robotics and artificial intelligence technologies are playing a critical role in this transition phase of our everyday lives. We saw the emergence of last-mile delivery robots on the street, AI-embedded service robots in restaurants, uninhabited shops, non-face-to-face medical services, conferences and talks in metaverses, and AI-based online education programs. This book is an edition that aims at serving researchers and practitioners in related fields with a timely dissemination of the recent progress in the areas of robotics and artificial intelligence. This book is based on a collection of papers presented at the 10th International Conference on Robot Intelligence Technology and Applications (RiTA), held at Griffith University in the Gold Coast, Queensland, Australia. The conference was held in a hybrid format on December 7–9, 2022, with the main theme “Artificial, Agile, Acute Robot Intelligence.” For better readability, the total of 41 papers are grouped into five chapters: Chapter I: Motion Planning and Control; Chapter II: Vision and Image Processing; Chapter III: Unmanned Aerial Vehicles and Autonomous Vehicles; Chapter IV: Learning and Classification; and Chapter V: Environmental and Societal Robotic Applications. The articles were accepted through a rigorous peer-review process and presented at the RiTA 2022 conference. Also, they were updated, and final versions of the manuscripts were produced after in-depth discussions during the conference. We would like to thank all the authors and editors for contributing to this edition.
Download or read book Efficient Memory based Learning for Robot Control written by Andrew William Moore and published by . This book was released on 1990 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt:
