Search results for: advances-in-robot-learning

Advances in Robot Learning

Author : Jeremy Wyatt
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This book constitutes the thoroughly refereed post-workshop proceedings of the 8th European Workshop on Learning Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.The seven revised full workshop papers presented were carefully reviewed and selected for inclusion in the book. Also included are two invited full papers. Among the topics addressed are map building for robot navigation, multi-task reinforcement learning, neural network approaches, example-based learning, situated agents, planning maps for mobile robots, path finding, autonomous robots, and biologically inspired approaches.

Recent Advances in Robot Learning

Author : Judy A. Franklin
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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).

Recent Advances in Robot Learning

Author : Judy A. Franklin
File Size : 67.64 MB
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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).

Advances in Robot Learning

Author : Jeremy Wyatt
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This book constitutes the thoroughly refereed post-workshop proceedings of the 8th European Workshop on Learning Robots, EWLR'99, held in Lausanne, Switzerland in September 1999. The seven revised full workshop papers presented were carefully reviewed and selected for inclusion in the book. Also included are two invited full papers. Among the topics addressed are map building for robot navigation, multi-task reinforcement learning, neural network approaches, example-based learning, situated agents, planning maps for mobile robots, path finding, autonomous robots, and biologically inspired approaches.

Advances in Robot Control

Author : Sadao Kawamura
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This volume surveys three decades of modern robot control theory and describes how the work of Suguru Arimoto shaped its development. Twelve survey articles written by experts associated with Suguru Arimoto at various stages in his career treat the subject comprehensively. This book provides an important reference for graduate students and researchers, as well as for mathematicians, engineers and scientists whose work involves robot control theory.

Advances in Robotics Research

Author : Torsten Kröger
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This historic book may have numerous typos and missing text. Purchasers can usually download a free scanned copy of the original book (without typos) from the publisher. Not indexed. Not illustrated. 1856 edition. Excerpt: ...required. Is there in this anything in more flagrant contradiction to the principles of Republican Freedom, or more dangerous to the public liberties, than in the system practised by the slaveholding interest represented in the General Government t But a third opportunity was close at hand, and Slavery made a third struggle for the extension of its domain and the enlargement of its power. The annexation of Texas involved us in war with Mexico. The war was waged on our part with vigor, skill, and success. It resulted in the cession to the United States of New Mexico, California, and Deseret, vast territories over which was extended by Mexican law a prohibition of Slavery. The slaveholders demanded access to them all, resisted the admission of California and New Mexico, which the energy of freemen, outstripping in its activity the Government, and even the slaveholding interest, had already converted into free States, and treasonably menaced Congress and the Union with overthrow, if its demands were not conceded. The free spirit of the country was roused with indignation by these pretensions, and for a time the whole nation roused to the tempest which they had created. Untoward events aided the wrong. The death of the President threw the whole power of the Administration into timid and faithless hands. Party resentments and party ambitions interposed against the right. Great men, leaders of the people, from whom, in better days, the people had learned lessons of principles and patriotism, yielded to the howlings of the storm, and sought shelter, in submission, from its rage. The slaveholding interest was again victorious. California, with her free1 constitution, was indeed admitted into the Union; but New Mexico, with her constitution...

Advances in the Integration of Brain Machine Interfaces and Robotic Devices

Author : Luca Tonin
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Intelligent Autonomous Systems 6

Author : Enrico Pagello
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After a long period, in which the research focused mainly on industrial robotics, nowadays scientists aim to build machines able to act autonomously in unstructured domains, and to interface friendly with humans, while performing intelligently their assigned tasks. Such intelligent autonomous systems are now being intensively developed, and are ready to be applied to every field, from social life to modern enterprises. We believe the following years will be increasingly characterised by their extensive use. This is dramatically changing the whole scenario of human society.

Design and Control Advances in Robotics

Author : Mellal, Mohamed Arezk
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Robotics plays a pivotal role in many domains such as industry and medicine. Robots allow for increased safety, production rates, accuracy, and quality; however, robots must be well designed and controlled to achieve the required performance. The design and control of robotics involve many varying disciplines, such as mechanical engineering, electronics, and automation, and must be further studied to ensure the technology is utilized appropriately. Design and Control Advances in Robotics considers the most recent applications and design advances in robotics and highlights the latest developments and applications within the field of robotics. Covering key topics such as deep learning, machine learning, programming, automation, and control advances, this reference work is ideal for engineers, computer scientists, industry professionals, academicians, practitioners, scholars, researchers, instructors, and students.

Intelligent Robotics and Applications

Author : Xin-Jun Liu
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The 4-volume set LNAI 13013 – 13016 constitutes the proceedings of the 14th International Conference on Intelligent Robotics and Applications, ICIRA 2021, which took place in Yantai, China, during October 22-25, 2021. The 299 papers included in these proceedings were carefully reviewed and selected from 386 submissions. They were organized in topical sections as follows: Robotics dexterous manipulation; sensors, actuators, and controllers for soft and hybrid robots; cable-driven parallel robot; human-centered wearable robotics; hybrid system modeling and human-machine interface; robot manipulation skills learning; micro_nano materials, devices, and systems for biomedical applications; actuating, sensing, control, and instrumentation for ultra-precision engineering; human-robot collaboration; robotic machining; medical robot; machine intelligence for human motion analytics; human-robot interaction for service robots; novel mechanisms, robots and applications; space robot and on-orbit service; neural learning enhanced motion planning and control for human robot interaction; medical engineering.