Robot Learning From Human Teachers

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.

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.

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.

In the past years there has been considerable effort to move robots from industrial environments to our daily lives where they can collaborate and interact with humans to improve our life quality. One of the key challenges in this direction is to make a suitable robot control system that can adapt to humans and interactively learn from humans to facilitate the efficient and safe co-existence of the two. The applications of such robotic systems include: service robotics and physical human-robot collaboration, assistive and rehabilitation robotics, semi-autonomous cars, etc. To achieve the goal of integrating robotic systems into these applications, several important research directions must be explored. One such direction is the study of skill transfer, where a human operator’s skilled executions are used to obtain an autonomous controller. Another important direction is shared control, where a robotic controller and humans control the same body, tool, mechanism, car, etc. Shared control, in turn invokes very rich research questions such as co-adaptation between the human and the robot, where the two agents can benefit from each other’s skills or must adapt to each other’s behavior to achieve effective cooperative task executions. The aim of this Research Topic is to help bridge the gap between the state-of-the-art and above-mentioned goals through novel multidisciplinary approaches in human-in-the-loop robot control and learning.

Can robots be teachable agents to students really? In the future, I believe robots have potential to contribute to education by acting as subordinate learners for students teaching. The benefits that the act of teaching provides for one's own learning have long been recognized, tutoring associated improvements in measures like achievement for the tutor role than the lecturer role. However, scientists had confirmed that human teaching robots has been concerned with learning for the benefit of the robot rather than that of the human. Intelligent autonomous robots are making their way into an increasing range of application areas: Manufacturing, transportation, surveillance and monitoring, rehabilitation, agriculture, service. Hence, if teaching robots can be confirmed to assist teachers to teach students in any schools. It seems that if can be applied to teach manufacturing workers how to produce any products in manufacturing process; it can be applied to teach drivers to drive cars, or teach pilots to control plane engineering machines to fly to sky. So, it can be applied to teach any occupation learners to learn how to operate any engineering machines to replace any human machine training teachers in possible. Hence, it is possible, future one day, robots can be any mahine operating occupation job trainees' teachers ( tutors) or trainers. In education industry aspect, for computer subject example, robotic systems have potential to increase studetn involvement and motivation and to improve the effectiveness of learning. Possible ways in which they could do so range from small personal robots used as teaching tools for areas like programming and computational thinking to move science-fictional future scenanios wirh humanoid robots supplement or even replacing teacher role in schools. Recently " teachable agents" have been used as a tool to help students learn, e.g. s student is tasked with training a simulated computer agent on course material, which can lead to a deeper and more committed understanding on the student's own part. For example, a robotic presence in classrooms may one day help to solve problems of teacher shortage, as learning-by-teaching systems have historically been used to do. And the data about human teaching styles that will be generated by widespread use of teachable robots in classroom settings will be invaluable in developing approaches to help robots learn more effectively from humans, where it is rapid and natural instruction of a robot in a new task. Consequently, whether future robot role is teacher role better or teacher's assistant role or tutor role better in education industry. It depends on the general student's preferable teaching choice in the school. Such as if the school's students prefer to be applied to be taught by human teachers. Then, robots can be choosed to be the school teachers' teaching assistant role to assist the school's teachers' teaching jobs only. Otherwise, if the school students prefer to be accepted to be taught by robots. Then, robots can be choosed to be the school teachers' role to replace human teachers to teach students in the school. Thus, future robots can be either teacher role or teacher's assistant or tutor role for child age, young age or adult age student learning consumers in any primary or secondary or university in future robot computer educational machine development.

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.

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