Download or read book Some Experiments with Reinforcement Learning on Real World Robots written by Shardul Vikram and published by . This book was released on 2001 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 1996-06-30 with total page 226 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 Explainable and Interpretable Reinforcement Learning for Robotics written by Aaron M. Roth and published by Springer Nature. This book was released on with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Inverse Reinforcement Learning for Robotic Applications written by Kenneth Daniel Bogert and published by . This book was released on 2016 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robots deployed into many real-world scenarios are expected to face situations that their designers could not anticipate. Machine learning is an effective tool for extending the capabilities of these robots by allowing them to adapt their behavior to the situation in which they find themselves. Most machine learning techniques are applicable to learning either static elements in an environment or elements with simple dynamics. We wish to address the problem of learning the behavior of other intelligent agents that the robot may encounter. To this end, we extend a well-known Inverse Reinforcement Learning (IRL) algorithm, Maximum Entropy IRL, to address challenges expected to be encountered by autonomous robots during learning. These include: occlusion of the observed agent's state space due to limits of the learner's sensors or objects in the environment, the presence of multiple agents who interact, and partial knowledge of other agents' dynamics. Our contributions are investigated with experiments using simulated and real world robots. These experiments include learning a fruit sorting task from human demonstrations and autonomously penetrating a perimeter patrol. Our work takes several important steps towards deploying IRL alongside other machine learning methods for use by autonomous robots.
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.
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 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 Deep Reinforcement Learning written by Aske Plaat and published by Springer Nature. This book was released on 2022-06-10 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.
Download or read book Imitation Learning from Observation written by Faraz Torabi and published by . This book was released on 2021 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in robotics have resulted in increases both in the availability of robots and also their complexity—a situation that necessitates automating both the execution and acquisition of robot behaviors. For this purpose, multiple machine learning frameworks have been proposed, including reinforcement learning and imitation learning. Imitation learning in particular has the advantage of not requiring a human engineer to attempt the difficult process of cost function design necessary in reinforcement learning. Moreover, compared to reinforcement learning, imitation learning typically requires less exploration time before an acceptable behavior is learned. These advantages exist because, in the framework of imitation learning, a learning agent has access to an expert agent that demonstrates how a task should be performed. Broadly speaking, this framework has a limiting constraint in that it requires the learner to have access not only to the states (e.g., observable quantities such as spatial location) of the expert, but also to its actions (e.g., internal control signals such as motor commands). This constraint is limiting in the sense that it prevents the agent from taking advantage of potentially rich demonstration resources that do not contain action information, e.g., YouTube videos. To alleviate this restriction, Imitation Learning from Observation (IfO) has recently been introduced as an imitation learning framework that explicitly seeks to learn behaviors by observing state-only expert demonstrations. The IfO problem has two main components: (1) perception of the demonstrations, and (2) learning a control policy. This thesis focuses primarily on the second component, and introduces multiple algorithms to solve the control aspect of the problem. Each of the proposed algorithms has certain advantages and disadvantages over the others in terms of performance, stability and sample complexity. Moreover, some of the algorithms are model-based (i.e., a model of the dynamics of the environment is learned in the imitation learning process), and some are model-free. In general, model-based algorithms are more sample-efficient, whereas model-free algorithms are known for their performance. Though the focus of this thesis is on the control aspect of IfO, two algorithms are introduced that do integrate a perception module into one of the control algorithms. By doing so, the adaptability of that control algorithm to the general IfO problem is shown. The work in this thesis is evaluated primarily in simulation, though in some cases experiments were carried out using real-world robots as well. The performance of the proposed algorithms is compared against well-known baselines and it is shown that they outperform the baselines in most cases
Download or read book Advances in Physical Agents II written by Luis M. Bergasa and published by Springer Nature. This book was released on 2020-11-02 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book reports on cutting-edge Artificial Intelligence (AI) theories and methods aimed at the control and coordination of agents acting and moving in a dynamic environment. It covers a wide range of topics relating to: autonomous navigation, localization and mapping; mobile and social robots; multiagent systems; human-robot interaction; perception systems; and deep-learning techniques applied to the robotics. Based on the 21st edition of the International Workshop of Physical Agents (WAF 2020), held virtually on November 19-20, 2020, from Alcalá de Henares, Madrid, Spain, this book offers a snapshot of the state-of-the-art in the field of physical agents, with a special emphasis on novel AI techniques in perception, navigation and human robot interaction for autonomous systems.
Download or read book Supervised Reinforcement Learning written by Karla Conn and published by VDM Publishing. This book was released on 2007 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Can machines be taught? If so, what methods are useful for teaching machines? Machine learning is a field focused on systems that can learn through their own experiences and evaluation. Programmers could encode all behaviors for a task, but this process quickly becomes limited to condensed problems. Therefore, scientists have turned to methods with adaptability, taking cues from biological systems (including the human brain) to solve more complex problems in varied environments. This book describes two experiments implementing supervised reinforcement learning on a real, mobile robot. One tests the robot's reliability in completing a navigation task it has been taught by a supervisor. The other, in which obstacles are placed along the path to the goal, measures the robot's robustness to changes in environment. Experimental analysis answered: How quickly can the robot find the goal? How much reward does the robot amass? How often does the robot fail in the task? How closely does the robot match the supervisor's actions? This book is addressed to those looking for means to teach robots about rewards/punishments, such as researchers in Robotics, Machine Learning, and Engineering.
Download or read book Living with Robots written by Ruth Aylett and published by MIT Press. This book was released on 2021-09-21 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: The truth about robots: two experts look beyond the hype, offering a lively and accessible guide to what robots can (and can't) do. There’s a lot of hype about robots; some of it is scary and some of it utopian. In this accessible book, two robotics experts reveal the truth about what robots can and can’t do, how they work, and what we can reasonably expect their future capabilities to be. It will not only make you think differently about the capabilities of robots; it will make you think differently about the capabilities of humans. Ruth Aylett and Patricia Vargas discuss the history of our fascination with robots—from chatbots and prosthetics to autonomous cars and robot swarms. They show us the ways in which robots outperform humans and the ways they fall woefully short of our superior talents. They explain how robots see, feel, hear, think, and learn; describe how robots can cooperate; and consider robots as pets, butlers, and companions. Finally, they look at robots that raise ethical and social issues: killer robots, sexbots, and robots that might be gunning for your job. Living with Robots equips readers to look at robots concretely—as human-made artifacts rather than placeholders for our anxieties. Find out: •Why robots can swim and fly but find it difficult to walk •Which robot features are inspired by animals and insects •Why we develop feelings for robots •Which human abilities are hard for robots to emulate
Download or read book Obituaries Kenneth Hopkins written by and published by . This book was released on 1988 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Human Robot Interaction Control Using Reinforcement Learning written by Wen Yu and published by John Wiley & Sons. This book was released on 2021-10-06 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.
Download or read book Humanoid robot control policy and interaction design a study on simulation to machine deployment written by Suman Deb and published by GRIN Verlag. This book was released on 2019-08-06 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technical Report from the year 2019 in the subject Engineering - Robotics, grade: 9, , language: English, abstract: Robotic agents can be made to learn various tasks through simulating many years of robotic interaction with the environment which cannot be made in case of real robots. With the abundance of a large amount of replay data and the increasing fidelity of simulators to implement complex physical interaction between the robots and the environment, we can make them learn various tasks that would require a lifetime to master. But, the real benefits of such training are only feasible, if it is transferable to the real machines. Although simulations are an effective environment for training agents, as they provide a safe manner to test and train agents, often in robotics, the policies trained in simulation do not transfer well to the real world. This difficulty is compounded by the fact that oftentimes the optimization algorithms based on deep learning exploit simulator flaws to cheat the simulator in order to reap better reward values. Therefore, we would like to apply some commonly used reinforcement learning algorithms to train a simulated agent modelled on the Aldebaran NAO humanoid robot. The problem of transferring the simulated experience to real life is called the reality gap. In order to bridge the reality gap between the simulated and real agents, we employ a Difference model which will learn the difference between the state distributions of the real and simulated agents. The robot is trained on two basic tasks of navigation and bipedal walking. Deep Reinforcement Learning algorithms such as Deep Q-Networks (DQN) and Deep Deterministic Policy Gradients(DDPG) are used to achieve proficiency in these tasks. We then evaluate the performance of the learned policies and transfer them to a real robot using a Difference model based on an addition to the DDPG algorithm.
Download or read book Approaches to Probabilistic Model Learning for Mobile Manipulation Robots written by Jürgen Sturm and published by Springer. This book was released on 2013-12-12 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents techniques that enable mobile manipulation robots to autonomously adapt to new situations. Covers kinematic modeling and learning; self-calibration; tactile sensing and object recognition; imitation learning and programming by demonstration.
