Download or read book Learning Mobile Manipulation written by David Joseph Watkins and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing mobile robots with the ability to manipulate objects has, despite decades of research, remained a challenging problem. The problem is approachable in constrained environments where there is ample prior knowledge of the environment layout and manipulatable objects. The challenge is in building systems that scale beyond specific situational instances and gracefully operate in novel conditions. In the past, researchers used heuristic and simple rule-based strategies to accomplish tasks such as scene segmentation or reasoning about occlusion. These heuristic strategies work in constrained environments where a roboticist can make simplifying assumptions about everything from the geometries of the objects to be interacted with, level of clutter, camera position, lighting, and a myriad of other relevant variables. The work in this thesis will demonstrate how to build a system for robotic mobile manipulation that is robust to changes in these variables. This robustness will be enabled by recent simultaneous advances in the fields of big data, deep learning, and simulation.

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.

Download or read book Modern Robotics written by Kevin M. Lynch and published by Cambridge University Press. This book was released on 2017-05-25 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modern and unified treatment of the mechanics, planning, and control of robots, suitable for a first course in robotics.

Download or read book Learning Mobile Manipulation Actions from Human Demonstrations an Approach to Learning and Augmenting Action Models and Their Integration Into Task Representations written by Tim Welschehold and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: While incredible advancements in robotics have been achieved over the last decade, direct physical interaction with an initially unknown and dynamic environment is still a challenging problem. In order to use robots as service assistants and take over household chores in the user's home environment, they must be able to perform goal directed manipulation tasks autonomously and further, learn these task intuitively from their owners. Consider for instance the task of setting a breakfast table: Although it is a relatively simple task for a human being, it poses some serious challenges to the robot. It must physically handle the users customized household environment and the objects therein, i.e., how can the items needed to set up the table be grasped and moved, how can the kitchen cabinets be opened, etc. Additionally the personal preferences of the user on how the breakfast table should be arranged must be respected. Due to the diverse characteristics of the custom objects and the individual human needs even a standard task like setting a breakfast table is impossible to pre-program before knowing the place of use and its occurrences. Therefore, the most promising way to engage robots as domestic help is to enable them to learn the tasks they should perform directly by their owners, without requiring the owner to possess any special knowledge of robotics or programming skills. Throughout this thesis we present various contributions addressing these challenges. Although learning from demonstration is a well-established approach to teaching robots without explicit programming, most approaches in literature for learning manipulation actions use kinesthetic training as these actions require thorough knowledge of the interactions between the robot and the object which can be learned directly by kinesthetic teaching since no abstraction is needed. In addition, in most current imitation learning approaches mobile platforms are not considered. In this thesis we present a novel approach to learn joint robot base and end-effector action models from observing demonstrations carried out by a human teacher. To achieve this we adapt trajectory data obtained from RGBD recordings of the human teacher performing the action to the capabilities of the robot. We formulate a graph optimization problem that the links the observed human trajectories with robot grasping capabilities and kinematic constraints between co-occurring base and gripper poses, allowing us to generate robot suitable trajectories. In a next step, we do not just learn individual manipulation actions, but to combine several actions into one task. Challenges arise from handling ambiguous goals and generalizing the task to new settings. We present an approach to learn both representations together from the same teacher demonstrations, one for individual mobile manipulation actions as described above, and one for the representation of the overall task intent. We leverage a framework based on Monte Carlo tree search to compute sequences of feasible actions imitating the teacher intention in new settings without explicitly specifying a task goal. In this way, we can reproduce complex tasks while ensuring that all composing actions are executable in the given setting. The mobile manipulation models mentioned above are encoded as dynamic systems to facilitate interaction with objects in world coordinates. However, this poses the challenge of translating kinematic constraints of the robot to the task space and including them in the action models. In this thesis we propose to couple robot base and end-effector motions generated by arbitrary dynamical systems by modulating the base velocity, while respecting the robots kinematic design. To this end we learn an approximation of the inverse reachability in closed form and implement the coupling as an obstacle avoidance problem. Furthermore, in this work we address the challenge of imitating manipulation actions, the execution of which depends on additional non-geometric quantities as, e.g., contact forces when handing over an object or measured liquid height, while pouring water into a cup. We suggest an approach to include this additional information in form of measured features directly into the action models. These features are recorded in the demonstrations alongside the geometric route of the manipulation action and their correlation is captured in a Gaussian Mixture Model that parametrizes the dynamic system used. This enables us to also couple the motion's geometric trajectory to the perceived features in the scene during action imitation. All the above described contributions were evaluated extensively in real world robot experiments on a PR2 system and a KUKA Iiwa Robot Arm

Download or read book Constructing Mobile Manipulation Behaviors Using Expert Interfaces and Autonomous Robot Learning written by Hai Dai Nguyen and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With current state-of-the-art approaches, development of a single mobile manipulation capability can be a labor-intensive process that presents an impediment to the creation of general purpose household robots. At the same time, we expect that involving a larger community of non-roboticists can accelerate the creation of new novel behaviors. We introduce the use of a software authoring environment called ROS Commander (ROSCo) allowing end-users to create, refine, and reuse robot behaviors with complexity similar to those currently created by roboticists. Akin to Photoshop, which provides end-users with interfaces for advanced computer vision algorithms, our environment provides interfaces to mobile manipulation algorithmic building blocks that can be combined and configured to suit the demands of new tasks and their variations. As our system can be more demanding of users than alternatives such as using kinesthetic guidance or learning from demonstration, we performed a user study with 11 able-bodied participants and one person with quadriplegia to determine whether computer literate non-roboticists will be able to learn to use our tool. In our study, all participants were able to successfully construct functional behaviors after being trained. Furthermore, participants were able to produce behaviors that demonstrated a variety of creative manipulation strategies, showing the power of enabling end-users to author robot behaviors. Additionally, we introduce how using autonomous robot learning, where the robot captures its own training data, can complement human authoring of behaviors by freeing users from the repetitive task of capturing data for learning. By taking advantage of the robot's embodiment, our method creates classifiers that predict using visual appearances 3D locations on home mechanisms where user constructed behaviors will succeed. With active learning, we show that such classifiers can be learned using a small number of examples. We also show that this learning system works with behaviors constructed by non-roboticists in our user study. As far as we know, this is the first instance of perception learning with behaviors not hand-crafted by roboticists.

Download or read book Developing a Mobile Manipulation System to Handle Unknown and Unstructured Objects written by Abdulrahman Al-Shanoon and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The exceptional human's ability to interact with unknown objects based on minimal prior experience is a permanent inspiration to the field of robotic manipulation. The recent revolution in industrial and service robots demands high-autonomy and intelligent mobile-manipulators. The goal of the thesis is to develop an autonomous mobile robotic manipulation system that can handle unknown and unstructured objects with the least training and human involvement. First, an end-to-end vision-based mobile manipulation architecture with minimal training using synthetic datasets is proposed in this thesis. The system includes: 1) effective training strategy of a perception network for object pose estimation, 2) the result is utilized as sensing feedback to integrate into a visual servoing system to achieve autonomous mobile manipulation. Experimental findings from simulations and real-world settings showed the efficiency of using computer-generated datasets, that can be generalized to the physical mobile-manipulator task. The model of the presented robot is experimentally verified and discussed. Second, a challenging robotic manipulation scenario of unknown-adjacent objects is addressed in this thesis by using a scalable self-supervised system that can learn grasping control strategies for unknown objects based on limited knowledge and simple sample objects. The developed learning scheme can be beneficial to both generalization and transferability without requiring any additional training or prior object awareness. Finally, an end-to-end self-learning framework is proposed to learn manipulating policies for challenging scenarios based on minimal training time and raw experience. The proposed model learns from scratch, from visual observations to sequential decision-making, manipulating actions and generalizes to unknown scenarios. The agent comprehends a sequence of manipulations that purposely lead to successful grasps. Results of the experiments demonstrated the effectiveness of the learning between manipulating actions, in which the grasping success rate has dramatically increased. The proposed system is successfully experimented and validated in simulations and real-world settings.

Download or read book Advanced Mobile Robotics written by DaeEun Kim and published by MDPI. This book was released on 2020-03-06 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective.

Download or read book Learning for Adaptive and Reactive Robot Control written by Aude Billard and published by MIT Press. This book was released on 2022-02-08 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Robotics Research written by Georges Giralt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 650 pages. Available in PDF, EPUB and Kindle. Book excerpt: This publication covers all the topics which are relevant to Advanced Robotics today, ranging from Systems Design to Reasoning and Planning. It is based on the Seventh International Symposium on Robotics Research held in Germany on October, 21 - 24th, 1995. The papers were written by specialists in the field from the United States, Europe, Japan, Australia and Canada. The editors, who also chaired this symposium, present the latest research results as well as new approaches to long standing problems. Robotics Research is a contribution to the emerging concepts, methods and tools that shape Robotics. The papers range from pure research reports to application-oriented studies. The topics covered include: manipulation, control, virtual reality, motion planning, 3D vision and industrial systems' issues.

Download or read book ROS Robotics Projects written by Lentin Joseph and published by Packt Publishing Ltd. This book was released on 2017-03-31 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build a variety of awesome robots that can see, sense, move, and do a lot more using the powerful Robot Operating System About This Book Create and program cool robotic projects using powerful ROS libraries Work through concrete examples that will help you build your own robotic systems of varying complexity levels This book provides relevant and fun-filled examples so you can make your own robots that can run and work Who This Book Is For This book is for robotic enthusiasts and researchers who would like to build robot applications using ROS. If you are looking to explore advanced ROS features in your projects, then this book is for you. Basic knowledge of ROS, GNU/Linux, and programming concepts is assumed. What You Will Learn Create your own self-driving car using ROS Build an intelligent robotic application using deep learning and ROS Master 3D object recognition Control a robot using virtual reality and ROS Build your own AI chatter-bot using ROS Get to know all about the autonomous navigation of robots using ROS Understand face detection and tracking using ROS Get to grips with teleoperating robots using hand gestures Build ROS-based applications using Matlab and Android Build interactive applications using TurtleBot In Detail Robot Operating System is one of the most widely used software frameworks for robotic research and for companies to model, simulate, and prototype robots. Applying your knowledge of ROS to actual robotics is much more difficult than people realize, but this title will give you what you need to create your own robotics in no time! This book is packed with over 14 ROS robotics projects that can be prototyped without requiring a lot of hardware. The book starts with an introduction of ROS and its installation procedure. After discussing the basics, you'll be taken through great projects, such as building a self-driving car, an autonomous mobile robot, and image recognition using deep learning and ROS. You can find ROS robotics applications for beginner, intermediate, and expert levels inside! This book will be the perfect companion for a robotics enthusiast who really wants to do something big in the field. Style and approach This book is packed with fun-filled, end-to-end projects on mobile, armed, and flying robots, and describes the ROS implementation and execution of these models.

Download or read book Learning the State of the World written by Lawson Lok Sang Wong and published by . This book was released on 2016 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile-manipulation robots performing service tasks in human-centric indoor environments have long been a dream for developers of autonomous agents. Tasks such as cooking and cleaning typically involve interaction with the environment, hence robots need to know relevant aspects of their spatial surroundings. However, service robots typically have little prior information about their environment, unlike industrial robots in structured environments. Even if this information was given initially, due to the involvement of other agents (e.g., humans adding/moving/removing objects), uncertainty in the complete state of the world is inevitable over time. Additionally, most information about the world is irrelevant to any particular task at hand. Mobile-manipulation robots therefore need to continuously perform the task of state estimation, using perceptual information to maintain a representation of the state, and its uncertainty, of task-relevant aspects of the world. Because indoor tasks frequently require interacting with objects, objects should be given critical emphasis in spatial representations for service robots. Compared to occupancy grids and feature-based maps that have been used traditionally in navigation and mapping, object-based representations are still in their infancy. By definition, mobile-manipulation robots are capable of moving in and interacting with the world. Hence, at the very least, such robots need to know about the physical occupancy of space and potential targets of interaction (i.e., objects). In this thesis, I propose a representation based on objects, their 'semantic' attributes (task-relevant properties such as type and pose), and their geometric realizations in the physical world.

Download or read book RoboCup 2019 Robot World Cup XXIII written by Stephan Chalup and published by Springer Nature. This book was released on 2019-11-30 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the post-conference proceedings of the 23rd RoboCup International Symposium, held in Sydney, NSW, Australia, in July 2019. The 38 full revised papers and 14 invited papers presented in this book were carefully reviewed and selected from 74 submissions. This book highlights the approaches of champion teams from the competitions and documents the proceedings of the 23rd annual RoboCup International Symposium. Due to the complex research challenges set by the RoboCup initiative, the RoboCup International Symposium offers a unique perspective for exploring scientific and engineering principles underlying advanced robotic and AI systems.

Download or read book A Mathematical Introduction to Robotic Manipulation written by Richard M. Murray and published by CRC Press. This book was released on 2017-12-14 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Mathematical Introduction to Robotic Manipulation presents a mathematical formulation of the kinematics, dynamics, and control of robot manipulators. It uses an elegant set of mathematical tools that emphasizes the geometry of robot motion and allows a large class of robotic manipulation problems to be analyzed within a unified framework. The foundation of the book is a derivation of robot kinematics using the product of the exponentials formula. The authors explore the kinematics of open-chain manipulators and multifingered robot hands, present an analysis of the dynamics and control of robot systems, discuss the specification and control of internal forces and internal motions, and address the implications of the nonholonomic nature of rolling contact are addressed, as well. The wealth of information, numerous examples, and exercises make A Mathematical Introduction to Robotic Manipulation valuable as both a reference for robotics researchers and a text for students in advanced robotics courses.

Download or read book Approaches to Probabilistic Model Learning for Mobile Manipulation Robots written by Jürgen Sturm and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Springer Handbook of Robotics written by Bruno Siciliano and published by Springer. This book was released on 2016-07-27 with total page 2259 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of this handbook provides a state-of-the-art overview on the various aspects in the rapidly developing field of robotics. Reaching for the human frontier, robotics is vigorously engaged in the growing challenges of new emerging domains. Interacting, exploring, and working with humans, the new generation of robots will increasingly touch people and their lives. The credible prospect of practical robots among humans is the result of the scientific endeavour of a half a century of robotic developments that established robotics as a modern scientific discipline. The ongoing vibrant expansion and strong growth of the field during the last decade has fueled this second edition of the Springer Handbook of Robotics. The first edition of the handbook soon became a landmark in robotics publishing and won the American Association of Publishers PROSE Award for Excellence in Physical Sciences & Mathematics as well as the organization’s Award for Engineering & Technology. The second edition of the handbook, edited by two internationally renowned scientists with the support of an outstanding team of seven part editors and more than 200 authors, continues to be an authoritative reference for robotics researchers, newcomers to the field, and scholars from related disciplines. The contents have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications. Further to an extensive update, fifteen new chapters have been introduced on emerging topics, and a new generation of authors have joined the handbook’s team. A novel addition to the second edition is a comprehensive collection of multimedia references to more than 700 videos, which bring valuable insight into the contents. The videos can be viewed directly augmented into the text with a smartphone or tablet using a unique and specially designed app. Springer Handbook of Robotics Multimedia Extension Portal: http://handbookofrobotics.org/

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