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Robot Learning from Human Demonstration

Author: Sonia Dechter

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 109

ISBN-13: 3031015703

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

Author: Chenguang Yang

Publisher: CRC Press

Published: 2021-06-21

Total Pages: 184

ISBN-13: 1000395170

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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

Author: Taylor Annette Kessler Faulkner

Publisher:

Published: 2022

Total Pages: 0

ISBN-13:

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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

Robot Programming by Demonstration

Author: Sylvain Calinon

Publisher: EPFL Press

Published: 2009-08-24

Total Pages: 248

ISBN-13: 9781439808672

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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.

Blocks to Robots

Author: Marina Umaschi Bers

Publisher:

Published: 2008

Total Pages: 176

ISBN-13:

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Contains examples of how robotics can be used in grades K through 2 as a hands-on tool for helping children learn about science, technology, engineering, and mathematics.

Robot Learning by Visual Observation

Author: Aleksandar Vakanski

Publisher: John Wiley & Sons

Published: 2017-01-13

Total Pages: 208

ISBN-13: 1119091780

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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

Learning from Human Teachers

Author: Bei Peng

Publisher:

Published: 2018

Total Pages: 162

ISBN-13:

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As the number of deployed robots grows, there will be an increasing need for humans to teach robots new skills that were not pre-programmed, without requiring these users to have any experience with programming or artificial intelligent systems. To enable this, we need to better understand how people want to teach and to support the ways in which people want to teach. To learn from human teachers, we consider the case where a human could provide online evaluative feedback, or design a sequence of tasks for the agent to learn on. With respect to learning from evaluative feedback, this dissertation demonstrates that learning algorithms that treat human feedback as a complex, discrete mode of communication can be better suited to learning from human trainers, rather than simply a numeric utility function to be optimized. Our empirical results indicate that humans, when teaching agents, deliver discrete feedback and follow different training strategies. We develop a novel model of evaluative feedback that captures knowledge about a teacher's training strategy. Based on this model, we develop two Bayesian learning algorithms that can learn from real users more efficiently than previous approaches that interpret feedback as numeric. To address limited evaluative feedback, we design a new representation of the learning agent. We demonstrate empirically that by changing the speed of the agent according to its confidence level, human trainers can be implicitly motivated to provide more explicit feedback when the learner has more uncertainty about how to act. We believe this can potentially be an effective way for the agent to interact with end-users, when taking into account human factors such as frustration. Finally, we consider the case where a human could design a sequence of tasks for the agent to learn on. We investigate how non-experts design curricula and how we can adapt machine-learning algorithms to better take advantage of this non-expert guidance. We empirically show that non-experts can design curricula that result in better overall agent performance than learning from scratch. We also demonstrate that by leveraging some principles people use when designing curricula, we can significantly improve our curriculum-learning algorithm.

Computational Human-Robot Interaction

Author: Andrea Thomaz

Publisher:

Published: 2016-12-20

Total Pages: 140

ISBN-13: 9781680832082

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Computational Human-Robot Interaction provides the reader with a systematic overview of the field of Human-Robot Interaction over the past decade, with a focus on the computational frameworks, algorithms, techniques, and models currently used to enable robots to interact with humans.