Machine Learning of Robot Assembly Plans PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Machine Learning of Robot Assembly Plans PDF full book. Access full book title Machine Learning of Robot Assembly Plans by Alberto Maria Segre. Download full books in PDF and EPUB format.
Author: Alberto Maria Segre Publisher: Springer Science & Business Media ISBN: 146131691X Category : Computers Languages : en Pages : 244
Book Description
The study of artificial intelligence (AI) is indeed a strange pursuit. Unlike most other disciplines, few AI researchers even agree on a mutually acceptable definition of their chosen field of study. Some see AI as a sub field of computer science, others see AI as a computationally oriented branch of psychology or linguistics, while still others see it as a bag of tricks to be applied to an entire spectrum of diverse domains. This lack of unified purpose among the AI community makes this a very exciting time for AI research: new and diverse projects are springing up literally every day. As one might imagine, however, this diversity also leads to genuine difficulties in assessing the significance and validity of AI research. These difficulties are an indication that AI has not yet matured as a science: it is still at the point where people are attempting to lay down (hopefully sound) foundations. Ritchie and Hanna [1] posit the following categorization as an aid in assessing the validity of an AI research endeavor: (1) The project could introduce, in outline, a novel (or partly novel) idea or set of ideas. (2) The project could elaborate the details of some approach. Starting with the kind of idea in (1), the research could criticize it or fill in further details (3) The project could be an AI experiment, where a theory as in (1) and (2) is applied to some domain. Such experiments are usually computer programs that implement a particular theory.
Author: Alberto Maria Segre Publisher: Springer Science & Business Media ISBN: 146131691X Category : Computers Languages : en Pages : 244
Book Description
The study of artificial intelligence (AI) is indeed a strange pursuit. Unlike most other disciplines, few AI researchers even agree on a mutually acceptable definition of their chosen field of study. Some see AI as a sub field of computer science, others see AI as a computationally oriented branch of psychology or linguistics, while still others see it as a bag of tricks to be applied to an entire spectrum of diverse domains. This lack of unified purpose among the AI community makes this a very exciting time for AI research: new and diverse projects are springing up literally every day. As one might imagine, however, this diversity also leads to genuine difficulties in assessing the significance and validity of AI research. These difficulties are an indication that AI has not yet matured as a science: it is still at the point where people are attempting to lay down (hopefully sound) foundations. Ritchie and Hanna [1] posit the following categorization as an aid in assessing the validity of an AI research endeavor: (1) The project could introduce, in outline, a novel (or partly novel) idea or set of ideas. (2) The project could elaborate the details of some approach. Starting with the kind of idea in (1), the research could criticize it or fill in further details (3) The project could be an AI experiment, where a theory as in (1) and (2) is applied to some domain. Such experiments are usually computer programs that implement a particular theory.
Author: Steven Minton Publisher: Morgan Kaufmann ISBN: 1483221172 Category : Social Science Languages : en Pages : 555
Book Description
Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.
Author: Aude Billard Publisher: MIT Press ISBN: 0262367017 Category : Technology & Engineering Languages : en Pages : 425
Book Description
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.
Author: Yves Kodratoff Publisher: Elsevier ISBN: 0080510558 Category : Computers Languages : en Pages : 836
Book Description
Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.
Author: Bob Bolles Publisher: World Scientific ISBN: 9814497258 Category : Computers Languages : en Pages : 478
Book Description
Rapid advances in sensors, computers, and algorithms continue to fuel dramatic improvements in intelligent robots. In addition, robot vehicles are starting to appear in a number of applications. For example, they have been installed in public settings to perform such tasks as delivering items in hospitals and cleaning floors in supermarkets; recently, two small robot vehicles were launched to explore Mars.This book presents the latest advances in the principal fields that contribute to robotics. It contains contributions written by leading experts addressing topics such as Path and Motion Planning, Navigation and Sensing, Vision and Object Recognition, Environment Modeling, and others.
Author: Ryszard S. Michalski Publisher: Morgan Kaufmann ISBN: 9781558602519 Category : Computers Languages : en Pages : 798
Book Description
Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area. See below for earlier volumes in the series.
Author: Judy A. Franklin Publisher: Springer Science & Business Media ISBN: 1461304717 Category : Computers Languages : en Pages : 218
Book Description
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).
Author: Maarten van Someren Publisher: Springer Science & Business Media ISBN: 9783540628583 Category : Computers Languages : en Pages : 380
Book Description
This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997. This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues.
Author: J. H. Connell Publisher: Springer Science & Business Media ISBN: 1461531845 Category : Technology & Engineering Languages : en Pages : 247
Book Description
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.
Author: Alberto Maria Segre
Author: Alberto Maria Segre
Author: Steven Minton
Author: Aude Billard
Author: Yves Kodratoff
Author: Bob Bolles
Author: Ryszard S. Michalski
Author: Judy A. Franklin
Author: Maarten van Someren
Author: Katia P. Sycara
Author: J. H. Connell