Human In The Loop Learning And Control For Robot Teleoperation

Human-in-the-loop Learning and Control for Robot Teleoperation presents recent, research progress on teleoperation and robots, including human-robot interaction, learning and control for teleoperation with many extensions on intelligent learning techniques. The book integrates cutting-edge research on learning and control algorithms of robot teleoperation, neural motor learning control, wave variable enhancement, EMG-based teleoperation control, and other key aspects related to robot technology, presenting implementation tactics, adequate application examples and illustrative interpretations. Robots have been used in various industrial processes to reduce labor costs and improve work efficiency. However, most robots are only designed to work on repetitive and fixed tasks, leaving a gap with the human desired manufacturing effect. Introduces research progress and technical contributions on teleoperation robots, including intelligent human-robot interactions and learning and control algorithms for teleoperation Presents control strategies and learning algorithms to a teleoperation framework to enhance human-robot shared control, bi-directional perception and intelligence of the teleoperation system Discusses several control and learning methods, describes the working implementation and shows how these methods can be applied to a specific and practical teleoperation system

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.

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.

This monograph presents innovative research regarding the body experience of human individuals who are using assistive robotic devices such as wearable robots or teleoperation systems. The focus is set on human-in-the-loop experiments that help to empirically evaluate how users experience devices. Moreover, these experiments allow for further examination of the underlying mechanisms of body experience through extending existing psychological paradigms, e.g., by disentangling tactile feedback from contacts. Besides reporting and discussing psychological examinations, the influence of various aspects of engineering design is investigated, e.g., different implementations of haptic interfaces or robot control. As haptics are of paramount importance in this tight type of human-robot interaction, it is explored with respect to modality as well as temporal and spatial effects. The first part of the book motivates the research topic and gives an in-depth analysis of the experimental requirements. The second and third part present experimental designs and studies of human-robot body experience regarding the upper and lower limbs as well as cognitive models to predict them. The fourth part discusses a multitude of design considerations and provides directions to guide future research on bidirectional human-machine interfaces and non-functional haptic feedback.

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.

This book presents the proceedings of the 32nd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD), held in Bled, Slovenia, June 14-16, 2023. It gathers contributions by researchers from several countries on all major areas of robotic research, development and innovation, as well as new applications and current trends. The topics covered include: novel designs and applications of robotic systems, intelligent cooperating and service robots, advanced robot control, human-robot interfaces, robot vision systems, mobile robots, humanoid and walking robots, bio-inspired and swarm robotic systems, aerial, underwater and spatial robots, robots for ambient assisted living, medical robots and bionic prostheses, cognitive robots, cloud robotics, ethical and social issues in robotics, etc. Given its scope, the book offers a source of information and inspiration for researchers seeking to improve their work and gather new ideas for future developments.

Robotic research and developments in computing technologies including artificial intelligence have led to significant improvements in autonomous capabilities of robots. Yet, human supervision is advisable and, in many cases, necessary when robots interact with real-world, outside-lab environments. This is due to the fact that complete autonomy in robots has not yet been achieved. When robots encounter challenges beyond their capabilities, a viable solution is to include human operators in the loop, who can support robots through teleoperation, taking complete control or shared control. This monograph focuses on a special form of shared control, namely mixed-initiative, where the final command to the robot is a weighted sum of the commands from the operator and the autonomous controller. The weights (fixed or adaptive), called authority allocation (AA) factors, decide who has more control authority over the robot. Several research groups use different methods to adapt the AA factors online and the benefits of adaptive mixed-initiative shared control have been well established in terms of task completion success and operator usability. However, stability of the overall shared control framework, with communication time-delays between the operator and the robot, is a field that has not been examined extensively. This monograph presents methods to improve performance and stability in shared control so that the possibilities of its applications can be widened. Firstly, methods to improve the haptic feedback performance of teleoperation are developed. Secondly, methods to stabilize adaptive shared control systems, while still ensuring high teleoperation performance, are proposed. The methods are validated on multiple robotic systems and they were applied in several projects, both in space and terrestrial domains. With the aforementioned contributions, this monograph provides an overarching framework to improve synergy between humans and robots. The flexibility of the framework allows integration of existent teleoperation and shared control approaches, which further promotes synergy within the robotics community.

The principal chapters of this book form a collection of technical articles sp- ning many areas of research in robotics, these are followed by a set of short r- iniscences and tributes written by former students of Professor George A. Bekey. Professor Bekey, a pioneer in robotics, retired from the University of Southern C- ifornia (USC) in 2002 after serving on its faculty for forty years. He maintains an association with USC as University Professor Emeritus. Professor Bekey turned 80 in June 2008 - this is his Festschrift. As one of Professor Bekey’s former students, it has been my privilege to know him for many years. This book represents the collective warm feelings of his former students, who remember their association with him in the fondest terms. Part I of this book is composed of technical chapters representing threads of active robotics research knitted loosely together. In many cases the themes of the chapters have their origins in the work the authors did when they were graduate students with Professor Bekey. These chapters are written for the reader interested in a sampling of modern research in Autonomous Robots. It is my hope that, for the serious reader, these chapters will serve as invitations to explore the ?eld via further reading and research.