Robot Learning from Human Demonstration

2022-06-01 Computers

Author : Sonia Dechter
Release : 2022-06-01
Genre : Computers
Kind : eBook
Book Rating : 703/5 ( reviews)

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Book Synopsis Robot Learning from Human Demonstration by : Sonia Dechter

Download or read book Robot Learning from Human Demonstration written by Sonia Dechter. This book was released on 2022-06-01. 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.

Robot Learning Human Skills and Intelligent Control Design

2021-06-21 Computers

Author : Chenguang Yang
Release : 2021-06-21
Genre : Computers
Kind : eBook
Book Rating : 170/5 ( reviews)

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Book Synopsis Robot Learning Human Skills and Intelligent Control Design by : Chenguang Yang

Download or read book Robot Learning Human Skills and Intelligent Control Design written by Chenguang Yang. This book was released on 2021-06-21. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning Robot Policies from Imperfect Human Teachers

2022

Author : Taylor Annette Kessler Faulkner
Release : 2022
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Learning Robot Policies from Imperfect Human Teachers by : Taylor Annette Kessler Faulkner

Download or read book Learning Robot Policies from Imperfect Human Teachers written by Taylor Annette Kessler Faulkner. This book was released on 2022. Available in PDF, EPUB and Kindle. Book excerpt: The ability to adapt and learn can help robots deployed in dynamic and varied environments. While in the wild, the data that robots have access to includes input from their sensors and the humans around them. The ability to utilize human data increases the usable information in the environment. However, human data can be noisy, particularly when acquired from non-experts. Rather than requiring expert teachers for learning robots, which is expensive, my research addresses methods for learning from imperfect human teachers. These methods use Human-in-the-loop Reinforcement Learning, which gives robots a reward function and input from human teachers. This dissertation shows that actively modifying which states receive feedback from imperfect, unmodeled human teachers can improve the speed and dependability of Human-In-the-loop Reinforcement Learning (HRL). This body of work addresses a bipartite model of imperfect teachers, in which humans can be inattentive or inaccurate. First, I present two algorithms for learning from inattentive teachers, which take advantage of intermittent attention from humans by adjusting state-action exploration to improve the learning speed of a Markovian HRL algorithm and give teachers more free time to complete other tasks. Second, I present two algorithms for learning from inaccurate teachers who give incorrect information to a robot. These algorithms estimate areas of the state space that are likely to receive incorrect feedback from human teachers, and can be used to filter messy, inaccurate data into information that is usable by a robot, performing dependably over a wide variety of inputs. The primary contribution of this dissertation is a set of algorithms that enable learning robots to adapt to imperfect teachers. These algorithms enable robots to learn policies more quickly and dependably than other existing HRL algorithms. My findings in HRL will enhance the ability of robots to learn new tasks from laypeople, requiring less time and knowledge of how to teach a robot than prior work. These advances are a step towards ubiquitous robot deployment in the home, public spaces, and other environments, with less demand for expensive expert data and an easier experience for novice robot users

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