Author: Sonia Dechter Publisher: Springer Nature ISBN: 3031015703 Category : Computers Languages : en Pages : 109
Book Description
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.
Author: Sylvain Calinon Publisher: EPFL Press ISBN: 9781439808672 Category : Computers Languages : en Pages : 248
Book Description
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.
Author: CHENGUANG. YANG Publisher: CRC Press ISBN: 9780367634377 Category : Languages : en Pages : 0
Book Description
This book focusses on robotic skill learning and intelligent control for robotic manipulators including enabling of robots to efficiently learn motor and stiffness/force regulation policies from humans. It explains transfer of human limb impedance control strategies to the robots so that the adaptive impedance control for the robot can be realized.
Author: Chenguang Yang Publisher: CRC Press ISBN: 1000395170 Category : Computers Languages : en Pages : 184
Book Description
In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task. This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user’s arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.
Author: Aude Billard Publisher: MIT Press ISBN: 0262367017 Category : Technology & Engineering Languages : en Pages : 425
Book Description
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.
Author: Chi Zhang Publisher: ISBN: Category : Human-robot interaction Languages : en Pages : 130
Book Description
Robot Learning from Demonstration (LfD) is a research area that focuses on how robots can learn new skills by observing how people perform various activities. As humans, we have a remarkable ability to imitate other human's behaviors and adapt to new situations. Endowing robots with these critical capabilities is a significant but very challenging problem considering the complexity and variation of human activities in highly dynamic environments. This research focuses on how robots can learn new skills by interpreting human activities, adapting the learned skills to new situations, and naturally interacting with humans. This dissertation begins with a discussion of challenges in each of these three problems. A new unified representation approach is introduced to enable robots to simultaneously interpret the high-level semantic meanings and generalize the low-level trajectories of a broad range of human activities. An adaptive framework based on feature space decomposition is then presented for robots to not only reproduce skills, but also autonomously and efficiently adjust the learned skills to new environments that are significantly different from demonstrations. To achieve natural Human Robot Interaction (HRI), this dissertation presents a Recurrent Neural Network based deep perceptual control approach, which is capable of integrating multi-modal perception sequences with actions for robots to interact with humans in long-term tasks. Overall, by combining the above approaches, an autonomous system is created for robots to acquire important skills that can be applied to human-centered applications. Finally, this dissertation concludes with a discussion of future directions that could accelerate the upcoming technological revolution of robot learning from human demonstration.
Author: Aleksandar Vakanski Publisher: John Wiley & Sons ISBN: 1119091780 Category : Technology & Engineering Languages : en Pages : 208
Book Description
This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstraction Discusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problem Focuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regression Concentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert
Author: Alexander Fabisch Publisher: ISBN: Category : Languages : en Pages :
Book Description
Reinforcement Learning; Imitation Learning; Embodiment Mapping; Contextual Policy Search; Manifold Learning; Robotics. - Behavior learning is a promising alternative to planning and control for behavior generation in robotics. The field is becoming more and more popular in applications where modeling the environment and the robot is cumbersome, difficult, or maybe even impossible. Learning behaviors for real robots that generalize over task parameters with as few interactions with the environment as possible is a challenge that this dissertation tackles. Which problems we can currently solve with behavior learning algorithms and which algorithms we need in the domain of robotics is not apparent at the moment as there are many related fields: imitation learning, reinforcement learning, self-supervised learning, and black-box optimization. After an extensive literature review, we decide to use methods from imitation learning and policy search to address the challenge. Specifically, we use human demonstrations recorded by motion capture systems and imitation learning with movement primitives to obtain initial behaviors that we later on generalize through contextual policy search. Imitation from motion capture data leads to the correspondence problem: the kinematic and dynamic capabilities of humans and robots are often fundamentally different and, hence, we have to compensate for that. This thesis proposes a procedure for automatic embodiment mapping through optimization and policy search and evaluates it with several robotic systems. Contextual policy search algorithms are often not sample efficient enough to learn directly on real robots. This thesis tries to solve the issue with active context selection, active training set selection, surrogate models, and manifold learning. The progress is illustrated with several simulated and real robot learning tasks. Strong connections between policy search and black-box optimization are revealed and exploited in this part of the thesis. This thesis demonstrates that learning manipulation behaviors is possible within a few hundred episodes directly on a real robot. Furthermore, these new approaches to imitation learning and contextual policy search are integrated in a coherent framework that can be used to learn new behaviors from human motion capture data almost automatically. Corresponding implementations that were developed during this thesis are available in an open source software.
Author: Zhiyong Chen Publisher: Springer ISBN: 3319975862 Category : Computers Languages : en Pages : 511
Book Description
The two volume set LNAI 10984 and LNAI 10985 constitutes the refereed proceedings of the 11th International Conference on Intelligent Robotics and Applications, ICIRA 2018, held in Newcastle, NSW, Australia, in August 2018. The 81 papers presented in the two volumes were carefully reviewed and selected from 129 submissions. The papers in the first volume of the set are organized in topical sections on multi-agent systems and distributed control; human-machine interaction; rehabilitation robotics; sensors and actuators; and industrial robot and robot manufacturing. The papers in the second volume of the set are organized in topical sections on robot grasping and control; mobile robotics and path planning; robotic vision, recognition and reconstruction; and robot intelligence and learning.
Author: Sonia Dechter
Author: Sylvain Calinon
Author: CHENGUANG. YANG
Author: Chernova
Author: Chenguang Yang
Author: Aude Billard
Author: Chi Zhang
Author: Aleksandar Vakanski
Author: Alexander Fabisch
Author: Zhiyong Chen