Download or read book Learning Perception and Motion Planning in Robotic Manipulation written by Weihao Yuan and published by . This book was released on 2020 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Perception based Learning for Fine Motion Planning in Robot Manipulation written by Enrique Cervera Mateu and published by . This book was released on 1997 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Perception Based Learning for Fine Motion Planning in Robot Manipulation written by and published by . This book was released on 1910 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Robots must successfully execute tasks in the presence of uncertainty. The main sources of uncertainty are modeling, sensing, and control. Fine motion problems involve a small-scale space and contact between objects. Though modern manipulators are very precise and repetitive, complex tasks may be difficult --or even impossible-- to model at the desired degree of exactitude; moreover, in real-world situations, the environment is not known a-priori and visual sensing does not provide enough accuracy. In order to develop successful strategies, it is necessary to understand what can be perceived, what action can be learnt --associated-- according to the perception, and how can the robot optimize its actions with regard to defined criteria. The thesis describes a robot programming architecture for learning fine motion tasks. Learning is an autonomous process of experience repetition, and the target is to achieve the goal in the minimum number of steps. Uncertainty in the location is assumed, and the robot is guided mainly by the sensory information acquired by a force sensor. The sensor space is analyzed by an unsupervised process which extracts features related with the probability distribution of the input samples. Such features are used to build a discrete state of the task to which an optimal action is associated, according to the past experience. The thesis also includes simulations of different sensory-based tasks to illustrate some aspects of the learning processes. The learning architecture is implemented on a real robot arm with force sensing capabilities. The task is a peg-in-hole insertion with both cylindrical and non-cylindrical workpieces.
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 Visual Perception and Robotic Manipulation written by Geoffrey Taylor and published by Springer. This book was released on 2008-08-18 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book moves toward the realization of domestic robots by presenting an integrated view of computer vision and robotics, covering fundamental topics including optimal sensor design, visual servo-ing, 3D object modelling and recognition, and multi-cue tracking, emphasizing robustness throughout. Covering theory and implementation, experimental results and comprehensive multimedia support including video clips, VRML data, C++ code and lecture slides, this book is a practical reference for roboticists and a valuable teaching resource.
Download or read book Generalizable Robot Manipulation Through Task and Motion Planning and Interactive Perception written by Xiaolin Fang (Researcher in electrical engineering and computer science) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a robot operating in a daily household environment, generality is of great importance. It should be able to generalize to different tasks that involve different objects in varying backgrounds and configurations. In this thesis, we will move towards this goal from two perspectives. We will first present a strategy for designing a robot manipulation system that can generalize to a wide range of goals, environments, and objects. Such generality is achieved through task and motion planning with affordances estimated by both learned and engineered modules. We demonstrate that this strategy can enable a single policy to perform a wide variety of real-world manipulation tasks. Next, we will present an interactive perception solution to deal with the uncertainty in the estimated affordances, with a focus on the segmentation of objects. We adopt an object-based belief representation to estimate the uncertainty coming from predicted segmentation, and select actions to reduce that efficiently. Our experiments show that our system can generalize better to different environments and reduce uncertainty more efficiently compared to our baselines.
Download or read book Visual Perception for Manipulation and Imitation in Humanoid Robots written by Pedram Azad and published by Springer Science & Business Media. This book was released on 2009-11-19 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dealing with visual perception in robots and its applications to manipulation and imitation, this monograph focuses on stereo-based methods and systems for object recognition and 6 DoF pose estimation as well as for marker-less human motion capture.
Download or read book Robot Programming by Demonstration written by Sylvain Calinon and published by EPFL Press. This book was released on 2009-08-24 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in RbD have identified a number of key issues for ensuring a generic approach to the transfer of skills across various agents and contexts. This book focuses on the two generic questions of what to imitate and how to imitate and proposes active teaching methods.
Download or read book Robotic Systems written by S.G. Tzafestas and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotics is a modern interdisciplinary field that has emerged from the marriage of computerized numerical control and remote manipulation. Today's robotic systems have intelligence features, and are able to perform dexterous and intelligent human-like actions through appropriate combination of learning, perception, planning, decision making and control. This book presents advanced concepts, techniques and applications reflecting the experience of a wide group of specialists in the field. Topics include: kinematics, dynamics, path planning and tracking, control, mobile robotics, navigation, robot programming, and sophisticated applications in the manufacturing, medical, and other areas.
Download or read book Mastering Robotics written by and published by Cybellium Ltd. This book was released on with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unveil the Frontiers of Robotic Innovation and Implementation In the realm of cutting-edge technology, robotics stands as a beacon of innovation with the potential to revolutionize industries and daily life. "Mastering Robotics" is your comprehensive guide to understanding and harnessing the power of robotics—a transformative field that spans science, engineering, and creativity. About the Book: As the boundaries of human achievement expand, robotics emerges as a dynamic field with diverse applications. "Mastering Robotics" offers a deep exploration of robotics technology—a cornerstone of modern automation and innovation. This book caters to both newcomers and experienced enthusiasts seeking to excel in robotics design, development, and deployment. Key Features: Robotics Fundamentals: Begin by understanding the core principles of robotics. Learn how robots function, their components, and how they interact with the world. Robotic Kinematics and Dynamics: Dive into the mechanics of robots. Explore kinematic chains, inverse kinematics, and the principles that govern robotic motion. Sensors and Perception: Grasp the art of integrating sensors into robots. Learn how robots perceive the world through sensors and understand their surroundings. Robot Programming: Explore the intricacies of programming robots. Understand how to write code to control robots' actions, movements, and responses. Robot Vision and Machine Learning: Delve into robotic vision and machine learning. Learn how robots process visual data and adapt their behavior using advanced algorithms. Robot Localization and Mapping: Grasp the significance of localization and mapping in robotics. Understand how robots navigate and build maps of their environments. Robotic Manipulation and Control: Explore techniques for robotic manipulation and control. Learn how robots interact with objects, perform tasks, and maintain stability. Real-World Applications: Gain insights into how robotics is applied across industries. From manufacturing to healthcare, discover the diverse applications of robotic technology. Why This Book Matters: In an era of technological advancement, mastering robotics offers a transformative advantage. "Mastering Robotics" empowers engineers, researchers, and technology enthusiasts to harness the potential of robotics, enabling them to innovate and create solutions that reshape industries and redefine human capabilities. Embark on a Journey of Innovation: In the landscape of cutting-edge technology, robotics holds the promise of reshaping our world. "Mastering Robotics" equips you with the knowledge needed to unlock the potential of robotics, enabling you to design, build, and deploy robotic systems that push the boundaries of human achievement. Whether you're a seasoned professional or a newcomer to robotics, this book will guide you in building a solid foundation for innovation and exploration. Your journey to mastering robotics starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Download or read book Practical Motion Planning in Robotics written by Kamal Gupta and published by Chichester, England ; Toronto : J. Wiley. This book was released on 1998-10-15 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Motion Planning in Robotics Current Approaches and Future Directions Edited by Kamal Gupta Simon Fraser University, Burnaby, Canada Angel P. del Pobil Jaume-l University, Castellon, Spain Designed to bridge the gap between research and industry, Practical Motion Planning in Robotics brings theoretical advances to bear on real-world applications. Capitalizing on recent progress, this comprehensive study emphasizes the practical aspects of techniques for collision detection, obstacle avoidance, path planning and manipulation planning. The broad approach spans both model- and sensor-based motion planning, collision detection and geometric complexity, and future directions. Features include: - Review of state-of-the-art techniques and coverage of the main issues to be considered in the development of motion planners for use in real applications - Focus on gross motion planning for articulated arms enabling robots to perform non-contact tasks with relatively high tolerances plus brief consideration of mobile robots - The use of efficient algorithms to tackle incremental changes in the environment - Illlustration of robot motion planning applications in virtual prototyping and the shipbuilding industry - Demonstration of efficient path planners combining both local and global planning approaches in conjunction with efficient techniques for collision detection and distance computations - International contributions from academia and industry Combining theory and practice, this timely book will appeal to academic researchers and practising engineers in the fields of robotic systems, mechatronics and computer science.
Download or read book Learning and Leveraging Kinematics for Robot Motion Planning Under Uncertainty written by Ajinkya Jain and published by . This book was released on 2021 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Service robots that can assist humans in performing day-to-day tasks will need to be general-purpose robots that can perform a wide array of tasks without much supervision from end-users. As they will be operating in unstructured and ever-changing human environments, they will need to be capable of adapting to their work environments quickly and learning to perform novel tasks within a few trials. However, current robots fall short of these requirements as they are generally highly specialized, can only perform fixed, predefined tasks reliably, and need to operate in controlled environments. One of the main reasons behind this big gap is that the current robots require complete and accurate information about their surroundings to function effectively, whereas, in human environments, robots will only have access to limited information about their tasks and environments. With incomplete information about its surroundings, a robot using pre-programmed or pre-learned motion policies will fail to adapt to the novel situations encountered during operation and fall short in completing its tasks. Online motion generation methods that do not reason about the lack of information will not suffice either, as the developed policies may be unreliable under incomplete information. Reasoning about the lack of information becomes critical for manipulation tasks a service robot would have to perform. These tasks will often require interacting with multiple objects that make or break contacts during the task. A contact between objects can significantly alter their subsequent motion and lead to sudden transitions in their dynamics. Under these sudden transitions, even minor errors in estimating object poses can cause drastic deviations from the robot's initial motion plan for the task and lead the robot to failure in completing the tasks. Hence, service robots need methods that generate motion policies for manipulation tasks efficiently while accounting for the uncertainty due to incomplete or partial information. Partially Observable Markov Decision Processes (POMDPs) is one such mathematical framework that can model and plan for tasks where the agent lacks complete information about the task. However, POMDPs incur exponentially increasing computational costs with planning time horizon, which restricts the current POMDP-based planning methods to problems having short time horizons. Another challenge for planning-based approaches is that they require a state transition function for the world they are operating in to develop motion plans, which may not always be available to the robot. In control theory terms, a state transition function for the world is analogous to its system plant. In this dissertation, we propose to address these challenges by developing methods that can learn state transition functions for robot manipulation tasks directly from observations and later use them to generate long-horizon motion plans to complete the task under uncertainty. We first model the world state transition functions for robot manipulation tasks involving sudden transitions, such as due to contacts, using hybrid models and develop a novel hierarchical POMDP-planner that leverages the representational power of hybrid models to develop motion plans for long-horizon tasks under uncertainty. Next, we address the requirement of planning-based methods to have access to world state transition functions. We introduce three novel methods for learning kinematic models for articulated objects directly from observations and present an algorithm to construct the state transition functions from the learned kinematics models for manipulating these objects. We focus on learning models for articulated objects as they form one of the biggest sets of household objects that service robots will frequently interact with. The first method, MICAH, focuses on learning kinematic models for articulated objects that exhibit configuration-dependent articulation properties, such as a refrigerator door that stays closed magnetically, from unsegmented sequences of observations of object part poses. Next, we introduce ScrewNet, which removes the requirement of object pose estimation of MICAH and learns articulation properties of objects directly from raw sensory data available to the robot (depth images) without knowing their articulation model category a priori. Extending it further, we introduce DUST-net, which learns distributions over articulation model parameters for objects indicating the network's confidence over the estimated parameters directly from raw depth images. Combining these methods, in this dissertation, we introduce a unified framework that can enable a robot to learn state transition functions for manipulation tasks from observations and later use them to develop long-horizon plans even under uncertainty
Download or read book Modelling Human Motion written by Nicoletta Noceti and published by Springer Nature. This book was released on 2020-07-09 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new frontiers of robotics research foresee future scenarios where artificial agents will leave the laboratory to progressively take part in the activities of our daily life. This will require robots to have very sophisticated perceptual and action skills in many intelligence-demanding applications, with particular reference to the ability to seamlessly interact with humans. It will be crucial for the next generation of robots to understand their human partners and at the same time to be intuitively understood by them. In this context, a deep understanding of human motion is essential for robotics applications, where the ability to detect, represent and recognize human dynamics and the capability for generating appropriate movements in response sets the scene for higher-level tasks. This book provides a comprehensive overview of this challenging research field, closing the loop between perception and action, and between human-studies and robotics. The book is organized in three main parts. The first part focuses on human motion perception, with contributions analyzing the neural substrates of human action understanding, how perception is influenced by motor control, and how it develops over time and is exploited in social contexts. The second part considers motion perception from the computational perspective, providing perspectives on cutting-edge solutions available from the Computer Vision and Machine Learning research fields, addressing higher-level perceptual tasks. Finally, the third part takes into account the implications for robotics, with chapters on how motor control is achieved in the latest generation of artificial agents and how such technologies have been exploited to favor human-robot interaction. This book considers the complete human-robot cycle, from an examination of how humans perceive motion and act in the world, to models for motion perception and control in artificial agents. In this respect, the book will provide insights into the perception and action loop in humans and machines, joining together aspects that are often addressed in independent investigations. As a consequence, this book positions itself in a field at the intersection of such different disciplines as Robotics, Neuroscience, Cognitive Science, Psychology, Computer Vision, and Machine Learning. By bridging these different research domains, the book offers a common reference point for researchers interested in human motion for different applications and from different standpoints, spanning Neuroscience, Human Motor Control, Robotics, Human-Robot Interaction, Computer Vision and Machine Learning. Chapter 'The Importance of the Affective Component of Movement in Action Understanding' of this book is available open access under a CC BY 4.0 license at link.springer.com.
Download or read book Mechanics of Robotic Manipulation written by Matthew T. Mason and published by MIT Press. This book was released on 2001-06-08 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: The science and engineering of robotic manipulation. "Manipulation" refers to a variety of physical changes made to the world around us. Mechanics of Robotic Manipulation addresses one form of robotic manipulation, moving objects, and the various processes involved—grasping, carrying, pushing, dropping, throwing, and so on. Unlike most books on the subject, it focuses on manipulation rather than manipulators. This attention to processes rather than devices allows a more fundamental approach, leading to results that apply to a broad range of devices, not just robotic arms. The book draws both on classical mechanics and on classical planning, which introduces the element of imperfect information. The book does not propose a specific solution to the problem of manipulation, but rather outlines a path of inquiry.
Download or read book Visuo tactile Perception for Dexterous Robotic Manipulation written by Maria Bauza Villalonga and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we develop visuo-tactile perception to enable general and precise robotic manipulation. In particular, we want to study how to effectively process visual and tactile information to allow robots to expand their capabilities while remaining accurate and reliable. We begin our work by focusing on developing tools for tactile perception. For the task of grasping, we use tactile observations to assess and improve grasp stability. Tactile information also allows extracting geometric information from contacts which is a task-independent feature. By learning to map tactile observations to contact shapes, we show robots can reconstruct accurate 3D models of objects, which can later be used for pose estimation. We build on the idea of using geometric information from contacts by developing tools that accurately render contact geometry in simulation. This enables us to develop a probabilistic approach to pose estimation for novel objects based on matching real visuo-tactile observations to a set of simulated ones. As a result, our method does not rely on real data and yields accurate pose distributions. Finally, we demonstrate how this approach to perception enables precise manipulations. In particular, we consider the task of precise pick-and-place of novel objects. Combining perception with task-aware planning, we build a robotic system that identifies in simulation which object grasps will facilitate grasping, planning, and perception; and selects the best one during execution. Our approach adapts to new objects by learning object-dependent models purely in simulation, allowing a robot to manipulate new objects successfully and perform highly accurate placements.
Download or read book Repetitive Motion Planning and Control of Redundant Robot Manipulators written by Yunong Zhang and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Repetitive Motion Planning and Control of Redundant Robot Manipulators presents four typical motion planning schemes based on optimization techniques, including the fundamental RMP scheme and its extensions. These schemes are unified as quadratic programs (QPs), which are solved by neural networks or numerical algorithms. The RMP schemes are demonstrated effectively by the simulation results based on various robotic models; the experiments applying the fundamental RMP scheme to a physical robot manipulator are also presented. As the schemes and the corresponding solvers presented in the book have solved the non-repetitive motion problems existing in redundant robot manipulators, it is of particular use in applying theoretical research based on the quadratic program for redundant robot manipulators in industrial situations. This book will be a valuable reference work for engineers, researchers, advanced undergraduate and graduate students in robotics fields. Yunong Zhang is a professor at The School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China; Zhijun Zhang is a research fellow working at the same institute.
Download or read book Introduction to Autonomous Mobile Robots second edition written by Roland Siegwart and published by MIT Press. This book was released on 2011-02-18 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive introduction to all aspects of mobile robotics, from algorithms to mechanisms. Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques. This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners. Curriculum developed by Dr. Robert King, Colorado School of Mines, and Dr. James Conrad, University of North Carolina-Charlotte, to accompany the National Instruments LabVIEW Robotics Starter Kit, are available. Included are 13 (6 by Dr. King and 7 by Dr. Conrad) laboratory exercises for using the LabVIEW Robotics Starter Kit to teach mobile robotics concepts.
