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Constructing Mobile Manipulation Behaviors Using Expert Interfaces and Autonomous Robot Learning

Constructing Mobile Manipulation Behaviors Using Expert Interfaces and Autonomous Robot Learning
Author: Hai Dai Nguyen
Publisher:
Total Pages:
Release: 2013
Genre: End-user computing
ISBN:
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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.

Approaches to Probabilistic Model Learning for Mobile Manipulation Robots

Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
Author: Jürgen Sturm
Publisher: Springer
Total Pages: 216
Release: 2013-12-12
Genre: Technology & Engineering
ISBN: 3642371604
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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.

Robot Telemanipulation in Unstructured Environments

Robot Telemanipulation in Unstructured Environments
Author: Adam Eric Leeper
Publisher:
Total Pages:
Release: 2013
Genre:
ISBN:
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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.

Learning Preference Models for Autonomous Mobile Robots in Complex Domains

Learning Preference Models for Autonomous Mobile Robots in Complex Domains
Author: David Silver
Publisher:
Total Pages: 182
Release: 2010
Genre: Machine learning
ISBN:
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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."

Advanced Guidance and Control Aspects in Robotics

Advanced Guidance and Control Aspects in Robotics
Author:
Publisher:
Total Pages: 236
Release: 1994
Genre: Artificial intelligence
ISBN:
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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.

Developing a Mobile Manipulation System to Handle Unknown and Unstructured Objects

Developing a Mobile Manipulation System to Handle Unknown and Unstructured Objects
Author: Abdulrahman Al-Shanoon
Publisher:
Total Pages: 0
Release: 2021
Genre:
ISBN:
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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.

Representing and Learning Affordance-based Behaviors

Representing and Learning Affordance-based Behaviors
Author: Tucker Ryer Hermans
Publisher:
Total Pages:
Release: 2014
Genre: Autonomous robots
ISBN:
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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.

Behavior Trees in Robotics and AI

Behavior Trees in Robotics and AI
Author: Michele Colledanchise
Publisher: CRC Press
Total Pages: 316
Release: 2018-07-20
Genre: Computers
ISBN: 0429950896
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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.

Introduction to Autonomous Mobile Robots, second edition

Introduction to Autonomous Mobile Robots, second edition
Author: Roland Siegwart
Publisher: MIT Press
Total Pages: 473
Release: 2011-02-18
Genre: Computers
ISBN: 0262295091
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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.

Learning for Adaptive and Reactive Robot Control

Learning for Adaptive and Reactive Robot Control
Author: Aude Billard
Publisher: MIT Press
Total Pages: 425
Release: 2022-02-08
Genre: Technology & Engineering
ISBN: 0262367017
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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.