Learning Based Robot Vision
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Learning Based Robot Vision
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Author : Josef Pauli
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-05-09
Learning Based Robot Vision written by Josef Pauli and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-05-09 with Computers categories.
This book provides the background and introduces a practical methodology for developing autonomous camera-equipped robot systems which solve deliberate tasks in open environments based on their competences acquired from training, interaction, and learning in the real task-relevant world; visual demonstration and neural learning for the backbone for acquiring the situated competences. The author verifies the practicability of the proposed methodology by presenting a structured case study including high-level sub-tasks such as localizing, approaching, grasping, and carrying objects.
Learning Based Robot Vision
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Author : Josef Pauli
language : en
Publisher: Springer
Release Date : 2014-03-12
Learning Based Robot Vision written by Josef Pauli and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03-12 with Computers categories.
Industrial robots carry out simple tasks in customized environments for which it is typical that nearly all e?ector movements can be planned during an - line phase. A continual control based on sensory feedback is at most necessary at e?ector positions near target locations utilizing torque or haptic sensors. It is desirable to develop new-generation robots showing higher degrees of autonomy for solving high-level deliberate tasks in natural and dynamic en- ronments. Obviously, camera-equipped robot systems, which take and process images and make use of the visual data, can solve more sophisticated robotic tasks. The development of a (semi-) autonomous camera-equipped robot must be grounded on an infrastructure, based on which the system can acquire and/or adapt task-relevant competences autonomously. This infrastructure consists of technical equipment to support the presentation of real world training samples, various learning mechanisms for automatically acquiring function approximations, and testing methods for evaluating the quality of the learned functions. Accordingly, to develop autonomous camera-equipped robot systems one must ?rst demonstrate relevant objects, critical situations, and purposive situation-action pairs in an experimental phase prior to the application phase. Secondly, the learning mechanisms are responsible for - quiring image operators and mechanisms of visual feedback control based on supervised experiences in the task-relevant, real environment. This paradigm of learning-based development leads to the concepts of compatibilities and manifolds. Compatibilities are general constraints on the process of image formation which hold more or less under task-relevant or accidental variations of the imaging conditions.
Learning Based Robot Vision
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Author : Josef Pauli
language : en
Publisher: Springer
Release Date : 2003-06-29
Learning Based Robot Vision written by Josef Pauli and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-29 with Computers categories.
Industrial robots carry out simple tasks in customized environments for which it is typical that nearly all e?ector movements can be planned during an - line phase. A continual control based on sensory feedback is at most necessary at e?ector positions near target locations utilizing torque or haptic sensors. It is desirable to develop new-generation robots showing higher degrees of autonomy for solving high-level deliberate tasks in natural and dynamic en- ronments. Obviously, camera-equipped robot systems, which take and process images and make use of the visual data, can solve more sophisticated robotic tasks. The development of a (semi-) autonomous camera-equipped robot must be grounded on an infrastructure, based on which the system can acquire and/or adapt task-relevant competences autonomously. This infrastructure consists of technical equipment to support the presentation of real world training samples, various learning mechanisms for automatically acquiring function approximations, and testing methods for evaluating the quality of the learned functions. Accordingly, to develop autonomous camera-equipped robot systems one must ?rst demonstrate relevant objects, critical situations, and purposive situation-action pairs in an experimental phase prior to the application phase. Secondly, the learning mechanisms are responsible for - quiring image operators and mechanisms of visual feedback control based on supervised experiences in the task-relevant, real environment. This paradigm of learning-based development leads to the concepts of compatibilities and manifolds. Compatibilities are general constraints on the process of image formation which hold more or less under task-relevant or accidental variations of the imaging conditions.
Intelligent Robotic Visual Perception With Deep Learning
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Author : Qiaokang Liang
language : en
Publisher: Elsevier
Release Date : 2025-09-01
Intelligent Robotic Visual Perception With Deep Learning written by Qiaokang Liang and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-01 with Technology & Engineering categories.
Intelligent Robotic Visual Perception with Deep Learning provides an in-depth exploration of deep learning-based robot Intelligent vision perception technologies that helps readers establish a solid foundation to learn about the applications and latest theoretical methods in visual perception. The book, in a comprehensive manner, covers the research aspects of deep learning technology in intelligent visual perception, ranging from methods to practical applications, algorithm analysis, and model construction. Users will find the latest international research trends that are essential for researchers working in the area. - Includes a detailed exploration of both algorithmic theory and practical applications - Provides a hands-on approach with case studies presented to help illustrate highly practical approaches - Shows readers how to construct intelligent robot vision perception systems tailored to real-world applications
Deep Learning In Vision Based Robotic Manipulation
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Author : Mengyuan Yan
language : en
Publisher:
Release Date : 2020
Deep Learning In Vision Based Robotic Manipulation written by Mengyuan Yan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
In the past decade, researchers are looking at bringing robots into our daily lives, and automating services such as taxis, delivery, house-works, and even medical procedures. One of the major roadblocks in making this leap is the diversity and uncertainty in the environments that the robots need to work in. Machine perception, i.e. understanding of the environment through visual, audio, and contact signals, is indispensable in such diverse and uncertain environments, and is a hard problem in itself. Further, the environment is changing, due to human activities and other factors, and robots need to react to the changes quickly. Recent developments in deep learning, especially computer vision, has brought us closer to achieving the goal of bringing robots into our daily environments. However, deep learning methods require a large amount of data with annotated labels, and new datasets and annotations need to be collected for each new task. Deep reinforcement learning algorithms have also achieved good performance on a range of locomotion or manipulation tasks, but the amount of interactions required to train most algorithms is so large that it could take days even with parallel simulation engines. Highly data-efficient models and learning algorithms are needed to help robots learn faster and with less human effort. Additionally, when designing a learning-based solution to a robotics task, inference speed needs to be taken into consideration so that the robot can respond to changes quickly. This thesis introduces methods to improve training data efficiency and inference speed for vision-based robotic manipulation. To improve data efficiency of models, we analyze properties and structures of the specific problems, and build structural biases into the models based on the insights obtained. In addition, we demonstrate self-supervised learning of the perception model on real images, enabling robots to collect their own training data without requiring human annotations. To improve robots' response speed, when learning motion policies we design learning algorithms to always explicitly learn the distribution of promising actions, instead of learning an action evaluation function which requires online optimization during runtime. The proposed methods are integrated into end-to-end systems and tested on real robots on two tasks: vision-based robotic grasping, and rope manipulation and knotting.
Learning Based Vision And Its Application To Autonomous Indoor Navigation
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Author : Shaoyun Chen (Engineer)
language : en
Publisher:
Release Date : 1998
Learning Based Vision And Its Application To Autonomous Indoor Navigation written by Shaoyun Chen (Engineer) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Mobile robots categories.
Robotic Vision Technologies For Machine Learning And Vision Applications
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Author : Garcia-Rodriguez, Jose
language : en
Publisher: IGI Global
Release Date : 2012-12-31
Robotic Vision Technologies For Machine Learning And Vision Applications written by Garcia-Rodriguez, Jose and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-31 with Technology & Engineering categories.
Robotic systems consist of object or scene recognition, vision-based motion control, vision-based mapping, and dense range sensing, and are used for identification and navigation. As these computer vision and robotic connections continue to develop, the benefits of vision technology including savings, improved quality, reliability, safety, and productivity are revealed. Robotic Vision: Technologies for Machine Learning and Vision Applications is a comprehensive collection which highlights a solid framework for understanding existing work and planning future research. This book includes current research on the fields of robotics, machine vision, image processing and pattern recognition that is important to applying machine vision methods in the real world.
Deep Learning For Robot Perception And Cognition
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Author : Alexandros Iosifidis
language : en
Publisher: Academic Press
Release Date : 2022-02-04
Deep Learning For Robot Perception And Cognition written by Alexandros Iosifidis and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-04 with Technology & Engineering categories.
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
Fourth Canadian Conference On Computer And Robot Vision
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Author :
language : en
Publisher: IEEE Computer Society Press
Release Date : 2007
Fourth Canadian Conference On Computer And Robot Vision written by and has been published by IEEE Computer Society Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
New Development In Robot Vision
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Author : Yu Sun
language : en
Publisher: Springer
Release Date : 2014-09-26
New Development In Robot Vision written by Yu Sun and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-26 with Technology & Engineering categories.
The field of robotic vision has advanced dramatically recently with the development of new range sensors. Tremendous progress has been made resulting in significant impact on areas such as robotic navigation, scene/environment understanding, and visual learning. This edited book provides a solid and diversified reference source for some of the most recent important advancements in the field of robotic vision. The book starts with articles that describe new techniques to understand scenes from 2D/3D data such as estimation of planar structures, recognition of multiple objects in the scene using different kinds of features as well as their spatial and semantic relationships, generation of 3D object models, approach to recognize partially occluded objects, etc. Novel techniques are introduced to improve 3D perception accuracy with other sensors such as a gyroscope, positioning accuracy with a visual servoing based alignment strategy for microassembly, and increasing object recognition reliability using related manipulation motion models. For autonomous robot navigation, different vision-based localization and tracking strategies and algorithms are discussed. New approaches using probabilistic analysis for robot navigation, online learning of vision-based robot control, and 3D motion estimation via intensity differences from a monocular camera are described. This collection will be beneficial to graduate students, researchers, and professionals working in the area of robotic vision.