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.

Download or read book Robot Learning Human Skills and Intelligent Control Design written by Chenguang Yang and published by CRC Press. This book was released on 2021-06-21 with total page 184 pages. 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.

Download or read book Learning Robot Policies from Imperfect Human Teachers written by Taylor Annette Kessler Faulkner and published by . This book was released on 2022 with total page 0 pages. 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

Download or read book Should Robots Replace Teachers written by Neil Selwyn and published by Polity. This book was released on 2019-11-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developments in AI, robotics and big data are changing the nature of education. Yet the implications of these technologies for the teaching profession are uncertain. While most educators remain convinced of the need for human teachers, outside the profession there is growing anticipation of a technological reinvention of the ways in which teaching and learning take place. Through an examination of technological developments such as autonomous classroom robots, intelligent tutoring systems, learning analytics and automated decision-making, Neil Selwyn highlights the need for nuanced discussions around the capacity of AI to replicate the social, emotional and cognitive qualities of human teachers. He pushes conversations about AI and education into the realm of values, judgements and politics, ultimately arguing that the integration of any technology into society must be presented as a choice. Should Robots Replace Teachers? is a must-read for anyone interested in the future of education and work in our increasingly automated times.

Download or read book Should Robots Replace Teachers written by Neil Selwyn and published by John Wiley & Sons. This book was released on 2019-10-11 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developments in AI, robotics and big data are changing the nature of education. Yet the implications of these technologies for the teaching profession are uncertain. While most educators remain convinced of the need for human teachers, outside the profession there is growing anticipation of a technological reinvention of the ways in which teaching and learning take place. Through an examination of technological developments such as autonomous classroom robots, intelligent tutoring systems, learning analytics and automated decision-making, Neil Selwyn highlights the need for nuanced discussions around the capacity of AI to replicate the social, emotional and cognitive qualities of human teachers. He pushes conversations about AI and education into the realm of values, judgements and politics, ultimately arguing that the integration of any technology into society must be presented as a choice. Should Robots Replace Teachers? is a must-read for anyone interested in the future of education and work in our increasingly automated times.

Download or read book Robot Programming by Demonstration written by Sylvain Calinon and published by EPFL Press. This book was released on 2009-08-24 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Robot Learning by Visual Observation written by Aleksandar Vakanski and published by John Wiley & Sons. This book was released on 2017-01-13 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Learning from Human Teachers written by Bei Peng and published by . This book was released on 2018 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Robots in Education written by Fady Alnajjar and published by Routledge. This book was released on 2021-07-29 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robots in Education is an accessible introduction to the use of robotics in formal learning, encompassing pedagogical and psychological theories as well as implementation in curricula. Today, a variety of communities across education are increasingly using robots as general classroom tutors, tools in STEM projects, and subjects of study. This volume explores how the unique physical and social-interactive capabilities of educational robots can generate bonds with students while freeing instructors to focus on their individualized approaches to teaching and learning. Authored by a uniquely interdisciplinary team of scholars, the book covers the basics of robotics and their supporting technologies; attitudes toward and ethical implications of robots in learning; research methods relevant to extending our knowledge of the field; and more.

Download or read book Computational Human Robot Interaction written by Andrea Thomaz and published by . This book was released on 2016-12-20 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Human inspired Robot Task Teaching and Learning written by Xianghai Wu and published by . This book was released on 2009 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current methods of robot task teaching and learning have several limitations: highly-trained personnel are usually required to teach robots specific tasks; service-robot systems are limited in learning different types of tasks utilizing the same system; and the teacher's expertise in the task is not well exploited. A human-inspired robot-task teaching and learning method is developed in this research with the aim of allowing general users to teach different object-manipulation tasks to a service robot, which will be able to adapt its learned tasks to new task setups. The proposed method was developed to be interactive and intuitive to the user. In a closed loop with the robot, the user can intuitively teach the tasks, track the learning states of the robot, direct the robot attention to perceive task-related key state changes, and give timely feedback when the robot is practicing the task, while the robot can reveal its learning progress and refine its knowledge based on the user's feedback. The human-inspired method consists of six teaching and learning stages: 1) checking and teaching the needed background knowledge of the robot; 2) introduction of the overall task to be taught to the robot: the hierarchical task structure, and the involved objects and robot hand actions; 3) teaching the task step by step, and directing the robot to perceive important state changes; 4) demonstration of the task in whole, and offering vocal subtask-segmentation cues in subtask transitions; 5) robot learning of the taught task using a flexible vote-based algorithm to segment the demonstrated task trajectories, a probabilistic optimization process to assign obtained task trajectory episodes (segments) to the introduced subtasks, and generalization of the taught task trajectories in different reference frames; and 6) robot practicing of the learned task and refinement of its task knowledge according to the teacher's timely feedback, where the adaptation of the learned task to new task setups is achieved by blending the task trajectories generated from pertinent frames. An agent-based architecture was designed and developed to implement this robot-task teaching and learning method. This system has an interactive human-robot teaching interface subsystem, which is composed of: a) a three-camera stereo vision system to track user hand motion; b) a stereo-camera vision system mounted on the robot end-effector to allow the robot to explore its workspace and identify objects of interest; and c) a speech recognition and text-to-speech system, utilized for the main human-robot interaction. A user study involving ten human subjects was performed using two tasks to evaluate the system based on time spent by the subjects on each teaching stage, efficiency measures of the robot's understanding of users' vocal requests, responses, and feedback, and their subjective evaluations. Another set of experiments was done to analyze the ability of the robot to adapt its previously learned tasks to new task setups using measures such as object, target and robot starting-point poses; alignments of objects on targets; and actual robot grasp and release poses relative to the related objects and targets. The results indicate that the system enabled the subjects to naturally and effectively teach the tasks to the robot and give timely feedback on the robot's practice performance. The robot was able to learn the tasks as expected and adapt its learned tasks to new task setups. The robot properly refined its task knowledge based on the teacher's feedback and successfully applied the refined task knowledge in subsequent task practices. The robot was able to adapt its learned tasks to new task setups that were considerably different from those in the demonstration. The alignments of objects on the target were quite close to those taught, and the executed grasping and releasing poses of the robot relative to objects and targets were almost identical to the taught poses. The robot-task learning ability was affected by limitations of the vision-based human-robot teleoperation interface used in hand-to-hand teaching and the robot's capacity to sense its workspace. Future work will investigate robot learning of a variety of different tasks and the use of more robot in-built primitive skills.

Download or read book Artificial Intelligence Brings Social Economic written by Johnny Ch LOK and published by . This book was released on 2020-07-29 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: In robots learning process, what difficulties which will face, the scientist indicated this question concerns how to influence robot's learning abilities: Has RL certain desirable qualifties, such as the learning abilities possibility to explore and learn from unsupervised experience? Many also queation RL as a variable technique for learning in complex real-world environments because of practical problems, such as long training time requirements, non-scaling state representations, sparse rewards ( resulting in slow utility propagation) and safe exploration strategies. As a result, reinforcement learning has been utilized for teaching robots and game characters, incorporating real-time human feedback by having a person supply reward and/or punishment as an additional input to the reward function.Consequently, the scientist discovered human's teaching method is the most important factor to influence the robot's learning abilities amonf all other environment factors. He also assumed and argued that reinforcement based learning approaches should be reformulated to move effectively incorporate a human teacher. To do this properly, the educational robot must understand the human teacher's contribution; how does the human teach? and what does the educational robot try to communicate from a robot learner? The scientist suggested human trainers ought use these methods to educate robot learners to learn more easily. His main findings indicates reward factor can influence the human robot teachers' motives to teach robot learners to learn, due to more reward can encourage the human robot trainers to teach robots to learn their knowledge and skill. He also found that robot users read the behavior of the robot machine learners and adjust the human robot trainer whose training strategies as whose mental model of the different robot human trainer education method changes. Viweing the human input as a traditional RL reward signal does not take advantage of the fact that a teacher adjusts hose training behavior to best suit the robot educational learner.In addition to the related RL works mentioned above. Every human robot trainer needs to consider the topic of human input for machine learning systems. Personalization agents and adaptive user interfaces are examples of software that learns by observing human behaviors modeling humann preferences or activities. It empathizes how human teaches the robot learner through interaction, various works address trainable software and robotic agents, exploring explicit human input: learning classification tasks and navigation tasks via natural language, robots that learn by demonstration or/and software agents that learn or training. It seems how to design the robot machine learning software technology will also influence the robot's learning abilities. Thus, human trainer's software learning machine and how to give reward to encourage the human trainer's teaching behavior to let the robot to learn, these factors will influence the robot's learning ability to be applied to education industry to assist human teacher's teaching job successfully. Because how much knowledge and skill, the robot can learn from the human trainer, how much educational knowledge that the robot can own to prepare to teach any students to learn easily. Hence, the human trainer's knowledge and skill will quality to satisfy its primary, secondary and university students learning need.⦁Can robots be teachable agents to students really?

Download or read book Artificial Intelligence Future Teaching And Writing Development written by Johnny Ch LOK and published by Independently Published. This book was released on 2020-05-28 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Psychological research (AI) educational social robots and students relationshipWhether can (AI) educational robots build good social relationship to students? Some scientists had attempted to do experiments to prove whether (AI) educational robots can do good or bad social relationship to students. For example, Knox, W.B. el. (2012) had ever attempted to do two experiments to research whether (AI) robot educational robot can build better or worse social relationship to compare human robot. Their two experiments aim to ask how differing conditions affect a human teacher's feedback frequency and the computational agent's learned performance. The first experiment considers the impact of a self-perceived teaching role in contrast to believing one is critiquing record. The second considers whether a human trainer will give more frequent feedback if the agent acts less ( i.e. choosing actions believed to be worse). When the trainer's recent feedback frequency decreases. From the results of those experiments, they draw three main conclusions that inform the design of agents. More broadly, these two studies indicate as early examples of a nascent technique of using agents as highly specifiable social entities in experiments on human behavior. Thus, it implies (AI) educational robots have ability to learn human teacher to teach students in good social relationship learning environment with students. Even, (AI) educational robots can build better learning relationship to compare human teachers between students, it seems (AI) robots have attractive ability t raise student individual learning interest, after which can applied to assist teachers to teach whose students in classrooms or lecture halls or online classroom channels.Other scientist had ever attempted to do experiments about " reinforcement learning" (RL) to research how result of interactive supervisory input between human teacher and both robot and software agents relationship. M. Mataric (1997) attempted to do one experiment concerns that reinforcement learning is designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. He aimed to examine the assumption, namely that the human-given reward is compatible with the traditionanl RL reward signal. He described an experimental platform with a simulated RL robot and present an analysis of real time human teaching behavior found in a study in which untrained subjects taught the robot to perform a new task. For the experiment, who reported three main observations on how people administer feedback when teaching a robot a task through reinforcement learning : (a) they use the reward channel not only for feedback, but also for future directed guidance, (b) they have a positive bias to their feedback, possibly using the signal as a motivational channel, and (c) they change their behavior as they develop a mental model of the robotic learner.

Download or read book Robot Assisted Learning and Education written by Agnese Augello and published by Frontiers Media SA. This book was released on 2021-01-04 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robots for Kids written by Allison Druin and published by Morgan Kaufmann. This book was released on 2000 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work brings together the insights of ten designers, researchers, and educators, each invited to contribute a chapter that relates his or her experience develping or using a children's robotic learning device. This growing area of endeavour is expected to have prodound and long-lasting effets on the ways children learn and develop, and its participants come from a wide range of backgrounds.

Download or read book Teaching Robotic Market Development written by Johnny Ch LOK and published by . This book was released on 2021-08-03 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether can (AI) educational robots bild good social relationsip to students? Some scientists had attempted to do experiments to prove whether (AI) educational robots can do good or bad social relationshp to students. For example, Knox, W.B. el. (2012) had ever attempted to do two experiments to research whether (AI) robot educational robot can build better or worse social relationship to compare human robot. Their two experiments aim to ask how differing conditions affect a human teacher's feedback frequency and the computational agent's learned performance. The first experiment considers the impact of a self-perceived teaching role in contrast to believing one is critiquing record. The second considers whether a human trainer will give more frequent feedback if the agent acts less ( i.e. choosing actions believed to be worse). When the trainer's recent feedback frequency decreases. From the results of those experiments, they draw three main conclusions that inform the design of agents. More broadly, these two studies indicate as early examples of a nascent technique of using agents as highly specifiable social entities in experiments on human behavior. Thus, it implies (AI) educational robots have ability to learn human teacher to teach students in good social relationship learning environment with students. Even, (AI) educational robots can build better learning relationship to compare human teachers between students, it seems (AI) robots have attractive ability t raise student individual learning interest, after which can applied to assist teachers to teach whose students in classrooms or lecture halls or online classroom channels. Other scientist had ever attempted to do experiments about " reinforcement learning" (RL) to research how result of interactive supervisory input between human teacher and both robot and software agents relationship. M. Mataric (1997) attempted to do one experiment concerns that reinforcement learning is designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. He aimed to examine the assumption, namely that the human-given reward is compatible with the traditionanl RL reward signal. He described an experimental platform with a simulated RL robot and present an analysis of real time human teaching behavior found in a study in which untrained subjects taught the robot to perform a new task. For the experiment, who reported three main observations on how people administer feedback when teaching a robot a task through reinforcement learning : (a) they use the reward channl not only for feedback, but also for future directed guideance, (b) they have a positive bias to their feedback, possibly using the signal as a motivational channel, and (c) they change their behavior as they develop a mental model of the robotic learner. Thus, whose experiment concluded that machine learning shall play a significant role in the development of robotic assistants that operate in human learning environment. ( e.g. homes, schools, hospitals, offices). Considering the difficulty of hard-coding all information needd for the robot to play a long term role in az dynamic world, human users will need to be able to easily teach such robots. However, various works have addressed some of hard problems robots face when learning in the real-word.

Download or read book Cognitive Computing for Human Robot Interaction written by Mamta Mittal and published by Academic Press. This book was released on 2021-08-13 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cognitive Computing for Human-Robot Interaction: Principles and Practices explores the efforts that should ultimately enable society to take advantage of the often-heralded potential of robots to provide economical and sustainable computing applications. This book discusses each of these applications, presents working implementations, and combines coherent and original deliberative architecture for human–robot interactions (HRI). Supported by experimental results, it shows how explicit knowledge management promises to be instrumental in building richer and more natural HRI, by pushing for pervasive, human-level semantics within the robot's deliberative system for sustainable computing applications. This book will be of special interest to academics, postgraduate students, and researchers working in the area of artificial intelligence and machine learning. Key features: - Introduces several new contributions to the representation and management of humans in autonomous robotic systems; - Explores the potential of cognitive computing, robots, and HRI to generate a deeper understanding and to provide a better contribution from robots to society; - Engages with the potential repercussions of cognitive computing and HRI in the real world. - Introduces several new contributions to the representation and management of humans in an autonomous robotic system - Explores cognitive computing, robots and HRI, presenting a more in-depth understanding to make robots better for society - Gives a challenging approach to those several repercussions of cognitive computing and HRI in the actual global scenario