Download or read book Learning to Adapt for Intelligent Robot Behavior written by Mengxi Li and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of robotics has been rapidly evolving in recent years, and robots are being used in an ever-increasing number of applications, from manufacturing to healthcare to household chores. One of the key challenges in robotics is enabling robots to perform complex manipulation tasks in unstructured and dynamic environments. While there have been significant advances in robot learning and control, many existing approaches are limited by their reliance on pre-defined motion primitives or generic models that do not account for the specific characteristics of individual users, other cooperative agents or the interacting objects. In order to be effective in these various settings, robots need to be able to adapt to different tasks and environments, and to interact with different types of agents, such as humans and other robots. This thesis investigates learning approaches for enabling robots to adapt their behavior in order to achieve intelligent robot behavior. In the first part of this thesis, we focus on enabling robots to better adapt to humans. We start by exploring how to leverage different sources of data to achieve personalization for human users. Firstly, we investigate how humans prefer to teleoperate assistive robot arms using low-dimensional controllers, such as joysticks. We present an algorithm that can efficiently develop personalized control for assistive robots. Here the data is obtained by initially demonstrating the behavior of the robot and then query the user to collect their corresponding preferred teleoperation control input from the joysticks. Subsequently, we delve into the exploration of leveraging weaker signals to infer information from agents, such as physical corrections. Experiment results indicate that human corrections are correlated and reasoning over these corrections together achieves improved accuracy. Finally, instead of only adapting to a single human user, we investigate how robots can more efficiently cooperate with and influence human teams by reasoning and exploiting the team structure. We apply our framework to two types of group dynamics, leading-following and predator-prey, and demonstrate that robots can first develop a group representation and utilize this representation to successfully influence a group to achieve various goals. In the second part of this thesis, we extend our investigation from human users to robot agents. We tackle the problem of how decentralized robot teams can adapt to each other by observing only the actions of other agents. We identify the problem of an infinite reasoning loop within the team and propose a solution by assigning different roles, such as "speaker" and "listener, " to the robot agents. This approach enables us to treat observed actions as a communication channel, thereby achieving effective collaboration within the decentralized team. Moving on to the third part of this thesis, we explore the topic of adapting to different tasks by developing customized tools. We emphasize the critical role of tools in determining how a robot interacts with objects, making them important in customizing robots for specific tasks. To address this, we present an end-to-end framework to automatically learn tool morphology for contact-rich manipulation tasks by leveraging differentiable physics simulators. Finally, we conclude the thesis by summarizing our efforts and discussing future directions.
Download or read book Robot Shaping written by Marco Dorigo and published by MIT Press. This book was released on 1998 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: foreword by Lashon Booker To program an autonomous robot to act reliably in a dynamic environment is a complex task. The dynamics of the environment are unpredictable, and the robots' sensors provide noisy input. A learning autonomous robot, one that can acquire knowledge through interaction with its environment and then adapt its behavior, greatly simplifies the designer's work. A learning robot need not be given all of the details of its environment, and its sensors and actuators need not be finely tuned. Robot Shaping is about designing and building learning autonomous robots. The term "shaping" comes from experimental psychology, where it describes the incremental training of animals. The authors propose a new engineering discipline, "behavior engineering," to provide the methodologies and tools for creating autonomous robots. Their techniques are based on classifier systems, a reinforcement learning architecture originated by John Holland, to which they have added several new ideas, such as "mutespec," classifier system "energy,"and dynamic population size. In the book they present Behavior Analysis and Training (BAT) as an example of a behavior engineering methodology.
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 Behavior Learning with Constructive Neural Networks in Mobile Robotics written by Jun Li and published by LAP Lambert Academic Publishing. This book was released on 2010-07 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: In behavior-based robotics, a robot achieves a required task by using various behaviors as the building blocks for that overall task. A robot behavior in turn is a sequence of sensory states and their corresponding motor actions, and extends in time and space. Making a robot able to learn (or develop) meaningful and purposeful behaviors from its own experiences has played one of the most important roles in intelligent robotics, and have been called the hallmark of intelligence. This book presents a learning system for acquiring robot behaviors by mapping sensor information directly to motor actions. It addresses the integration of three learning paradigms, namely unsupervised learning, supervised learning, and reinforcement learning. The approach is characterized by the use of constructive artificial neural networks, Several novel techniques for robot learning using constructive radial basis function networks are introduced. The learning system is verified by a number of experiments involving a real robot learning different behaviors. It is shown that the learning system is useful as a generic learning component for acquiring diverse behaviors in mobile robots.
Download or read book Robot Learning from Human Demonstration written by Sonia Dechter and published by Springer Nature. This book was released on 2022-06-01 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Learning Motor Skills written by Jens Kober and published by Springer. This book was released on 2013-11-23 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor. skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author’s doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award.
Download or read book Interactive Learning and Adaptation for Personalized Robot assisted Training written by Konstantinos Tsiakas and published by . This book was released on 2019 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot-Assisted Training (RAT) is a growing body of research in Human-Robot Interaction (HRI) that studies how robots can assist humans during a physical or cognitive training task. Robot-Assisted Training systems have a wide range of applications,varying from physical and/or social assistance in post-stroke rehabilitation to intervention and therapy for children with Autism Spectrum Disorders. The main goal of such systems is to provide a personalized and tailored session that matches user abilities and needs, by adjusting task-related parameters (e.g., task difficulty, robot behavior), in order to enhance the effects of the training session. Moreover, such systems need to adapt their training strategy based on user's affective and cognitive states. Considering the sequential nature of human-robot interactions, Reinforcement Learning (RL) is an appropriate machine learning paradigm for solving sequential decision making problems with the potential to develop adaptive robots that adjust their behavior based on human abilities, preferences and needs. This research is motivated by the challenges that arise when different types of users are considered for real-time personalization using Reinforcement Learning, in a Robot-Assisted Training scenario. To this end, we present an Interactive Learning and Adaptation Framework for Personalized Robot-Assisted Training. This framework utilizes Interactive RL (IRL)methods to facilitate the adaptation of the robot to each individual, monitoring both behavioral (task performance) and physiological data (task engagement). We discuss how task engagement can be integrated to the personalization mechanism, through Learning from Feedback. Moreover, we show how Human-in-the-Loop approaches can be used to utilize human expertise using informative control interfaces, towards a safe and tailored interaction. We illustrate this framework with a Socially Assistive Robotic (SAR) system that instructs and monitors a cognitive training task and adjusts task diculty and robot behavior, in order to provide a personalized training session. We present our data-driven approach (data collection, data analysis, user modeling and simulation), as well as a user study to evaluate our real-time SAR-based prototype system for personalized cognitive training. We discuss the limitations and challenges of our approach, as well as possible future directions, considering the different modules of the proposed system (RL-based personalization, user modeling,EEG analysis, Human-in-the-Loop). The long-term goal of this research is to develop personalized and co-adaptive human-robot interactive systems, where both agents(human, robot) adapt and learn from each other, in order to establish an efficient interaction.
Download or read book Interdisciplinary Approaches to Robot Learning written by John Demiris and published by World Scientific. This book was released on 2000 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation 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. Thereis 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.
Download or read book Reinforcement Learning of Bimanual Robot Skills written by Adrià Colomé and published by Springer Nature. This book was released on 2019-08-27 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book tackles all the stages and mechanisms involved in the learning of manipulation tasks by bimanual robots in unstructured settings, as it can be the task of folding clothes. The first part describes how to build an integrated system, capable of properly handling the kinematics and dynamics of the robot along the learning process. It proposes practical enhancements to closed-loop inverse kinematics for redundant robots, a procedure to position the two arms to maximize workspace manipulability, and a dynamic model together with a disturbance observer to achieve compliant control and safe robot behavior. In the second part, methods for robot motion learning based on movement primitives and direct policy search algorithms are presented. To improve sampling efficiency and accelerate learning without deteriorating solution quality, techniques for dimensionality reduction, for exploiting low-performing samples, and for contextualization and adaptability to changing situations are proposed. In sum, the reader will find in this comprehensive exposition the relevant knowledge in different areas required to build a complete framework for model-free, compliant, coordinated robot motion learning.
Download or read book Behavior based Robotics written by Ronald C. Arkin and published by MIT Press. This book was released on 1998 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foreword by Michael Arbib This introduction to the principles, design, and practice of intelligent behavior-based autonomous robotic systems is the first true survey of this robotics field. The author presents the tools and techniques central to the development of this class of systems in a clear and thorough manner. Following a discussion of the relevant biological and psychological models of behavior, he covers the use of knowledge and learning in autonomous robots, behavior-based and hybrid robot architectures, modular perception, robot colonies, and future trends in robot intelligence. The text throughout refers to actual implemented robots and includes many pictures and descriptions of hardware, making it clear that these are not abstract simulations, but real machines capable of perception, cognition, and action.
Download or read book Designing Robot Behavior in Human Robot Interactions written by Changliu Liu and published by CRC Press. This book was released on 2019-09-12 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we have set up a unified analytical framework for various human-robot systems, which involve peer-peer interactions (either space-sharing or time-sharing) or hierarchical interactions. A methodology in designing the robot behavior through control, planning, decision and learning is proposed. In particular, the following topics are discussed in-depth: safety during human-robot interactions, efficiency in real-time robot motion planning, imitation of human behaviors from demonstration, dexterity of robots to adapt to different environments and tasks, cooperation among robots and humans with conflict resolution. These methods are applied in various scenarios, such as human-robot collaborative assembly, robot skill learning from human demonstration, interaction between autonomous and human-driven vehicles, etc. Key Features: Proposes a unified framework to model and analyze human-robot interactions under different modes of interactions. Systematically discusses the control, decision and learning algorithms to enable robots to interact safely with humans in a variety of applications. Presents numerous experimental studies with both industrial collaborative robot arms and autonomous vehicles.
Download or read book Human in the loop System Design and Control Adaptation for Behavior Assistant Robots written by Yuquan Leng and published by Frontiers Media SA. This book was released on 2024-06-03 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the progress and development of human-robot systems, the coordination among humans, robots, and environments has become increasingly sophisticated. In this Research Topic, we focus on an important field in robotics and automation disciplines, which is commonly defined as behavior-assistant robots. The scope includes but is not limited to: (1) rehabilitation assistive devices, such as rigid/soft exoskeletons, prosthetic systems, orthoses, and intelligent wheelchairs; (2) intelligent medical systems, such as endoscopic robots, surgical robots, and the navigation systems; (3) industrial application devices, such as collaborative manipulators, load-bearing exoskeletons, supernumerary robotic limbs; (4) intelligent domestic devices, such as mobile robots, elderly-care robots, walking-aids robots and so on. The emergence of robot-assisted daily behaviors, based on aforementioned devices, is gradually becoming part of our social lives, which can improve weak motor abilities, enhance physical functionalities, and enable various other benefits.
Download or read book Robot Learning written by J. H. Connell and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Leveraging Program Synthesis for Robust Long term Robot Autonomy Via Interactive Learning and Adaptation written by Jarrett Holtz and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For autonomous robots to become as pervasive in uncontrolled human environments and our everyday lives as they are on campuses and in warehouses, they need to be deployable by end-users for various tasks. End-user deployment of autonomous robots over the long term requires robust behaviors that can leverage fundamental robot capabilities to achieve diverse goals while subject to various domains and user preferences. Achieving this goal requires a system for designing and adapting behaviors that is intuitive, data-efficient, easy to integrate, and can handle changes in user-imposed requirements over long deployments. State-of-the-art approaches to designing robot behaviors broadly fall into three categories: reinforcement learning, inverse reinforcement learning, and learning from demonstration. State-of-the-art approaches for these techniques widely leverage deep neural networks (DNNs) as function approximators to represent either the complete behavior, an optimal reward function, or both a value function and the behavior in Actor-Critic approaches. DNNs are a powerful tool for function approximation that have been the catalyst for significant successes across a wide range of learning applications. While DNN-based approaches are broadly applicable, they suffer from three key weaknesses when used for end-user robot behavior design and adaptation: 1) DNNs are black-box behavior representations and thus are opaque to the user and difficult to understand or verify, 2) learning with DNNs is extremely data-intensive, often requiring that data be collected in simulation, and 3) DNN behaviors are difficult to adapt and sensitive to changing domains or user- preferences, such as when transferring from simulation to the real world. In this thesis, we present approaches to leverage program synthesis as an alternative function approximator for learning from demonstration to approximate behaviors and reward functions, respectively. Program synthesis as a function approximator addresses some limitations of DNN-based approaches by yielding human-readable behavior representations that are amenable to program repair and parameter optimization for adaptation, and that can leverage the well-structured space of programs to learn behaviors in a data-efficient manner. However, due to two primary factors, existing state-of-the-art synthesis approaches are insufficient to learn general robot programs. First, these approaches are not designed to handle non-linear real arithmetic, vector operations, or dimensioned quantities, all commonly found in robot programs. Second, synthesis techniques are primarily limited by their ability to scale with the search space of potential programs, such that synthesis of many reasonably complex behaviors is intractable for existing approaches. To address the goal of end-user-guided robot behavior learning and adaption, We present Physics Informed Programs Synthesis (PIPS) as part of a learning from demonstration and adaptation approach to lifelong robot learning. Towards this goal, this thesis presents the following contributions: 1) An algorithm for PIPS that addresses limitations of program synthesis for robotics by reasoning about physical quantities, 2) algorithms for LfD leveraging PIPS to learn robot behaviors as human-readable programs, 3) an approach to guiding lifelong robot learning by leveraging the structure of programmatic policies and demonstrations, 4) program repair and synthesis techniques for adapting these learned policies from iterative user guidance, and finally, 5) extensive evaluation results in the social robot navigation domain across simulated and real-world deployments that compare PIPS-based learning to DNN-based and traditional approaches
Download or read book Value and Reward Based Learning in Neurobots written by Jeffrey L Krichmar and published by Frontiers Media SA. This book was released on 2015-03-05 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organisms are equipped with value systems that signal the salience of environmental cues to their nervous system, causing a change in the nervous system that results in modification of their behavior. These systems are necessary for an organism to adapt its behavior when an important environmental event occurs. A value system constitutes a basic assumption of what is good and bad for an agent. These value systems have been effectively used in robotic systems to shape behavior. For example, many robots have used models of the dopaminergic system to reinforce behavior that leads to rewards. Other modulatory systems that shape behavior are acetylcholine’s effect on attention, norepinephrine’s effect on vigilance, and serotonin’s effect on impulsiveness, mood, and risk. Moreover, hormonal systems such as oxytocin and its effect on trust constitute as a value system. This book presents current research involving neurobiologically inspired robots whose behavior is: 1) Shaped by value and reward learning, 2) adapted through interaction with the environment, and 3) shaped by extracting value from the environment.
Download or read book Robot Learning Human Skills and Intelligent Control Design written by Chenguang Yang and published by CRC Press. This book was released on 2021-06-21 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book TEXPLORE Temporal Difference Reinforcement Learning for Robots and Time Constrained Domains written by Todd Hester and published by Springer. This book was released on 2013-06-22 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agent’s lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples.
