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 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 Learning Preference Models for Autonomous Mobile Robots in Complex Domains written by David Silver and published by . This book was released on 2010 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Achieving robust and reliable autonomous operation even in complex unstructured environments is a central goal of field robotics. As the environments and scenarios to which robots are applied have continued to grow in complexity, so has the challenge of properly defining preferences and tradeoffs between various actions and the terrains they result in traversing. These definitions and parameters encode the desired behavior of the robot; therefore their correctness is of the utmost importance. Current manual approaches to creating and adjusting these preference models and cost functions have proven to be incredibly tedious and time-consuming, while typically not producing optimal results except in the simplest of circumstances. This thesis presents the development and application of machine learning techniques that automate the construction and tuning of preference models within complex mobile robotic systems. Utilizing the framework of inverse optimal control, expert examples of robot behavior can be used to construct models that generalize demonstrated preferences and reproduce similar behavior. Novel learning from demonstration approaches are developed that offer the possibility of significantly reducing the amount of human interaction necessary to tune a system, while also improving its final performance. Techniques to account for the inevitability of noisy and imperfect demonstration are presented, along with additional methods for improving the efficiency of expert demonstration and feedback. The effectiveness of these approaches is confirmed through application to several real world domains, such as the interpretation of static and dynamic perceptual data in unstructured environments and the learning of human driving styles and maneuver preferences. Extensive testing and experimentation both in simulation and in the field with multiple mobile robotic systems provides empirical confirmation of superior autonomous performance, with less expert interaction and no hand tuning. These experiments validate the potential applicability of the developed algorithms to a large variety of future mobile robotic systems."

Download or read book Modelling and Controlling of Behaviour for Autonomous Mobile Robots written by Hendrik Skubch and published by Springer Science & Business Media. This book was released on 2012-11-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: As research progresses, it enables multi-robot systems to be used in more and more complex and dynamic scenarios. Hence, the question arises how different modelling and reasoning paradigms can be utilised to describe the intended behaviour of a team and execute it in a robust and adaptive manner. Hendrik Skubch presents a solution, ALICA (A Language for Interactive Cooperative Agents) which combines modelling techniques drawn from different paradigms in an integrative fashion. Hierarchies of finite state machines are used to structure the behaviour of the team such that temporal and causal relationships can be expressed. Utility functions weigh different options against each other and assign agents to different tasks. Finally, non-linear constraint satisfaction and optimisation problems are integrated, allowing for complex cooperative behaviour to be specified in a concise, theoretically well-founded manner.

Download or read book Robot Telemanipulation in Unstructured Environments written by Adam Eric Leeper and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents methods for robot teleoperation, or equivalently, human-in-the- loop robotics. Human-in-the loop systems have the potential to handle complex tasks by combining the cognitive skills of a human operator with autonomous tools and behaviors. Along these lines, we present novel methods in grasp planning, haptic (force-feedback) rendering, and robot control which allow synergy in interaction between a human operator and a robot. We describe the interfaces that employ these algorithms, and validate them through user experiments. Our goal is to see robot technologies make a bigger impact in peoples' everyday lives, getting robots out of the laboratory and factory, and into homes, offices, and other unstructured human spaces. Our algorithms focus on three distinct areas of telerobotic manipulation but are unified by their common reliance on 3D point cloud data obtained from emerging sensor technol- ogy; we do not depend on environment or object models known a priori since it difficult to anticipate the things a robot will encounter in unstructured settings. First, since grasp- ing is a prerequisite for many manipulation tasks, we present two algorithms for planning grasps on clusters of 3D points. Next, we explore how to perform force-feedback haptic rendering of 3D point cloud data. This enables an operator to use the sense of touch to learn about environment geometry and potential collisions. Finally, we present a controller that uses a sequence of convex optimization steps to produce constrained arm motions that follow time-varying goal poses commanded by an operator. Using 3D sensor data to form motion constraints in real-time, the robot is responsive to changing goals from the user yet also avoids collisions and unfavorable arm configurations. We demonstrate the integration of our algorithms into a telerobotic system that enables an operator to perform varied and unscripted manipulation tasks in arbitrary settings. We describe tools for navigation, perception, and manipulation, ranging from direct control of a gripper or mobile base to autonomous sub-modules that perform collision-free base navigation or arm motion planning. Most importantly, we share results from testing these interfaces in a variety of settings, including user studies with non-expert operators and a case study with a motor-impaired operator using the robot in his own home.

Download or read book Behavior Learning with Constructive Neural Networks in Mobile Robotics written by Jun Li and published by LAP Lambert Academic Publishing. This book was released on 2010-07 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: In behavior-based robotics, a robot achieves a required task by using various behaviors as the building blocks for that overall task. A robot behavior in turn is a sequence of sensory states and their corresponding motor actions, and extends in time and space. Making a robot able to learn (or develop) meaningful and purposeful behaviors from its own experiences has played one of the most important roles in intelligent robotics, and have been called the hallmark of intelligence. This book presents a learning system for acquiring robot behaviors by mapping sensor information directly to motor actions. It addresses the integration of three learning paradigms, namely unsupervised learning, supervised learning, and reinforcement learning. The approach is characterized by the use of constructive artificial neural networks, Several novel techniques for robot learning using constructive radial basis function networks are introduced. The learning system is verified by a number of experiments involving a real robot learning different behaviors. It is shown that the learning system is useful as a generic learning component for acquiring diverse behaviors in mobile robots.

Download or read book Advanced Guidance and Control Aspects in Robotics written by and published by . This book was released on 1994 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: To ensure the capability of defense, a demand for equipment and systems which can be embraced under the title of "Robotics" will emerge in the near future. In this context, "Robotics" represents a specific problem area involving all the guidance and control functions which are associated with achieving goal-oriented autonomous behavior in structured and unstructured environments for mobile and manipulator systems as applied to ground, sea, air, and space operations. Related robotic systems must combine constituent functions such as intelligent decision making, control, manipulation, motion, sensing, and communication. The scope of the special course will cover new developments in the areas of autonomous navigation for planetary and surface systems, and control and operations of remote manipulators.

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 Behavior Trees in Robotics and AI written by Michele Colledanchise and published by CRC Press. This book was released on 2018-07-20 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Behavior Trees (BTs) provide a way to structure the behavior of an artificial agent such as a robot or a non-player character in a computer game. Traditional design methods, such as finite state machines, are known to produce brittle behaviors when complexity increases, making it very hard to add features without breaking existing functionality. BTs were created to address this very problem, and enables the creation of systems that are both modular and reactive. Behavior Trees in Robotics and AI: An Introduction provides a broad introduction as well as an in-depth exploration of the topic, and is the first comprehensive book on the use of BTs. This book introduces the subject of BTs from simple topics, such as semantics and design principles, to complex topics, such as learning and task planning. For each topic, the authors provide a set of examples, ranging from simple illustrations to realistic complex behaviors, to enable the reader to successfully combine theory with practice. Starting with an introduction to BTs, the book then describes how BTs relate to, and in many cases, generalize earlier switching structures, or control architectures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. The book then presents a set of important extensions and provides a set of tools for formally analyzing these extensions using a state space formulation of BTs. With the new analysis tools, the book then formalizes the descriptions of how BTs generalize earlier approaches and shows how BTs can be automatically generated using planning and learning. The final part of the book provides an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion. This book targets a broad audience, including both students and professionals interested in modeling complex behaviors for robots, game characters, or other AI agents. Readers can choose at which depth and pace they want to learn the subject, depending on their needs and background.

Download or read book Representing and Learning Affordance based Behaviors written by Tucker Ryer Hermans and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous robots deployed in complex, natural human environments such as homes and offices need to manipulate numerous objects throughout their deployment. For an autonomous robot to operate effectively in such a setting and not require excessive training from a human operator, it should be capable of discovering how to reliably manipulate novel objects it encounters. We characterize the possible methods by which a robot can act on an object using the concept of affordances. We define affordance-based behaviors as object manipulation strategies available to a robot, which correspond to specific semantic actions over which a task-level planner or end user of the robot can operate. This thesis concerns itself with developing the representation of these affordance- based behaviors along with associated learning algorithms. We identify three specific learning problems. The first asks which affordance-based behaviors a robot can successfully apply to a given object, including ones seen for the first time. Second, we examine how a robot can learn to best apply a specific behavior as a function of an object's shape. Third, we investigate how learned affordance knowledge can be transferred between different objects and different behaviors. We claim that decomposing affordance-based behaviors into three separate factors -- a control policy, a perceptual proxy, and a behavior primitive -- aids an autonomous robot in learning to manipulate. Having a varied set of affordance-based behaviors available allows a robot to learn which behaviors perform most effectively as a function of an object's identity or pose in the workspace. For a specific behavior a robot can use interactions with previously encountered objects to learn to robustly manipulate a novel object when first encountered. Finally, our factored representation allows a robot to transfer knowledge learned with one behavior to effectively manipulate an object in a qualitatively different manner by using a distinct controller or behavior primitive. We evaluate all work on a bimanual, mobile-manipulator robot. In all experiments the robot interacts with real-world objects sensed by an RGB-D camera.

Download or read book Wearable Technology for Robotic Manipulation and Learning written by Bin Fang and published by Springer. This book was released on 2021-10-08 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, millions of people, with varying backgrounds and levels of technical expertise, will have to effectively interact with robotic technologies on a daily basis. This means it will have to be possible to modify robot behavior without explicitly writing code, but instead via a small number of wearable devices or visual demonstrations. At the same time, robots will need to infer and predict humans’ intentions and internal objectives on the basis of past interactions in order to provide assistance before it is explicitly requested; this is the basis of imitation learning for robotics. This book introduces readers to robotic imitation learning based on human demonstration with wearable devices. It presents an advanced calibration method for wearable sensors and fusion approaches under the Kalman filter framework, as well as a novel wearable device for capturing gestures and other motions. Furthermore it describes the wearable-device-based and vision-based imitation learning method for robotic manipulation, making it a valuable reference guide for graduate students with a basic knowledge of machine learning, and for researchers interested in wearable computing and robotic learning.

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.

Download or read book Robot Learning from Interactions with Physics realistic Environment Constructing Big Task Platform for Training AI Agents written by Xu Xie and published by . This book was released on 2021 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot learning from interactions is a crucial topic in the joint field of computer vision, robotics, and machine learning. Interactions are ubiquitous in daily life, concrete instances comprise object-object, robot-object, and robot-robot interactions. Learning from interactions to an intelligent robot system is important because it helps the robot to generate a sense of physics, meanwhile planning and acting reasonably. To achieve this purpose, one primary challenge that remains in the community is the absence of dataset that can be leveraged to study the diverse categories of interactions. To create those datasets, the interaction data should be realistic such that it reflects the underlying physical process. Further, we argue that learning interactions through simulations is a promising approach to synthesize and scale up diverse forms of interactions. This dissertation focuses on robot learning from interactions in Mixed Reality (MR) as well as leveraging the state-of-the-art physical simulation to construct virtual environments to afford Big Tasks. There are four major contributions along this pathway: 1. Robot learning object manipulation skills from human demonstrations. Instead of directly learning from a robot-object manipulation dataset that is hard to generalize, we alternatively seek an approach to create a human-object manipulation dataset and let the robot learn from the demonstration. We claim that the key attribute of building such dataset embodies the realistic hand-object interaction that involves a setup that can faithfully capture the fine-grained raw motion signals. This leads us to develop a tactile glove system and collect informative spatial-temporal sensory data during hand manipulations. An event parsing pipeline is proposed upon the hand interactions that are transferable to the robot's end and learn the manipulation skill. 2. A virtual testbed to construct rich interactive tasks. The major limitation of collecting real-world interaction data can be summarized as three folds: i) a specific setup is needed to trace one form of interaction, ii) amount of efforts need to spend on data cleaning and labeling, and iii) a single dataset is not capable to capture different modalities of interactions at the same time. To overcome those issues, we propose and develop a virtual testbed, VRGym platform, for realistic human-robot interactive tasks (Big Tasks). In VRGym, the pipelines we developed are able to synthesize diverse photo-realistic 3D scenes that incorporate various forms of interactions through physics-based simulation. Given available rich interactions, we expect to grow a general-purpose agent from the interactive tasks and advance the research areas of robotics, machine learning as well as cognitive science. 3. Robot learning from imperfect demonstrations --- small data. In the area of learning from demonstration, interacting with objects, one essential element is the creation of expert demonstrations. However, non-trivial efforts are needed when collecting those demonstrations and a large portion of them contains failure cases. We develop the demonstration setup for learning objects grasping skills upon VRGym platform with VR human interfaces. Human performers interact with the virtual scene by teleoperating the virtual robot arm. At the same time, the demonstration is evaluated through physics simulation such that even a perfect task plan may fail during the execution. Given the sparsity of demonstrations, we think the failed ones are valuable in addition to the perfect demonstration. This enlightens us to exploit the implicit characteristics of small data in the presence of imperfect demonstrations. 4. A game platform for large-scale social interactions. Social interactions are another important branch that goes beyond physical only interactions. To develop a general-purpose agent, it has to properly infer other agents motion or intentions and applies socially acceptable behaviors when interacting in the scene. Inspired by those facts, we leverage a popular computer game platform, Grand Theft Auto (GTA), to automatically construct fruitful realistic social interactions in the simulated urban scenarios. The city transportation system, including vehicles and pedestrians, can be fully controlled by the developed modding scripts. The GTA platform is a supplement to VRGym that extends robot learning from interactions to a larger scale. We utilize it to synthesize multi-vehicle driving scenarios and study the problem of trajectories prediction as to the basis of intentions inference. We highlight the safety aspect by predicting collision-free trajectories that accord with the social norm for vehicle driving.

Download or read book Human robot Interaction written by Michael A. Goodrich and published by Now Publishers Inc. This book was released on 2007 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a unified treatment of HRI-related issues, identifies key themes, and discusses challenge problems that are likely to shape the field in the near future. The survey includes research results from a cross section of the universities, government efforts, industry labs, and countries that contribute to HRI.

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 The Robotics Primer written by Maja J. Mataric and published by MIT Press. This book was released on 2007-08-17 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: A broadly accessible introduction to robotics that spans the most basic concepts and the most novel applications; for students, teachers, and hobbyists. The Robotics Primer offers a broadly accessible introduction to robotics for students at pre-university and university levels, robot hobbyists, and anyone interested in this burgeoning field. The text takes the reader from the most basic concepts (including perception and movement) to the most novel and sophisticated applications and topics (humanoids, shape-shifting robots, space robotics), with an emphasis on what it takes to create autonomous intelligent robot behavior. The core concepts of robotics are carried through from fundamental definitions to more complex explanations, all presented in an engaging, conversational style that will appeal to readers of different backgrounds. The Robotics Primer covers such topics as the definition of robotics, the history of robotics (“Where do Robots Come From?”), robot components, locomotion, manipulation, sensors, control, control architectures, representation, behavior (“Making Your Robot Behave”), navigation, group robotics, learning, and the future of robotics (and its ethical implications). To encourage further engagement, experimentation, and course and lesson design, The Robotics Primer is accompanied by a free robot programming exercise workbook that implements many of the ideas on the book on iRobot platforms. The Robotics Primer is unique as a principled, pedagogical treatment of the topic that is accessible to a broad audience; the only prerequisites are curiosity and attention. It can be used effectively in an educational setting or more informally for self-instruction. The Robotics Primer is a springboard for readers of all backgrounds—including students taking robotics as an elective outside the major, graduate students preparing to specialize in robotics, and K-12 teachers who bring robotics into their classrooms.

Download or read book Robot Learning from Human Demonstration written by Sonia Dechter and published by Springer Nature. This book was released on 2022-06-01 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.