Learning a Set of Interrelated Tasks by Using a Succession of Motor Policies for a Socially Guided Intrinsically Motivated Learner

We aim at a robot capable to learn sequences of actions to achieve a field of complex tasks. In this paper, we are considering the learning of a set of interrelated complex tasks hierarchically organized. To learn this high-dimensional mapping between a continuous high-dimensional space of tasks and an infinite dimensional space of unbounded sequences of actions, we introduce a new framework called “procedures”, which enables the autonomous discovery of how to combine previously learned skills in order to learn increasingly complex combinations of motor policies. We propose an active learning algorithmic architecture, capable of organizing its learning process in order to achieve a field of complex tasks by learning sequences of primitive motor policies. Based on heuristics of active imitation learning, goal-babbling and strategic learning using intrinsic motivation, our algorithmic architecture leverages our procedures framework to actively decide during its learning process which outcome to focus on and which exploration strategy to apply. We show on a simulated environment that our new architecture is capable of tackling the learning of complex motor policies by adapting the complexity of its policies to the task at hand. We also show that our “procedures” enable the learning agent to discover the task hierarchy and exploit his experience of previously learned skills to learn new complex tasks

Should artificial agents ask for help in human-robot collaborative problem-solving?

International audienceTransferring as fast as possible the functioning of our brain to artificial intelligence is an ambitious goal that would help advance the state of the art in AI and robotics. It is in this perspective that we propose to start from hypotheses derived from an empirical study in a human-robot interaction and to verify if they are validated in the same way for children as for a basic reinforcement learning algorithm. Thus, we check whether receiving help from an expert when solving a simple close-ended task (the Towers of Hanoï) allows to accelerate or not the learning of this task, depending on whether the intervention is canonical or requested by the player. Our experiences have allowed us to conclude that, whether requested or not, a Q-learning algorithm benefits in the same way from expert help as children do

Effizientes und stabiles online Lernen für "Developmental Robots"

Recent progress in robotics and cognitive science has inspired a new generation of more versatile robots, so-called developmental robots. Many learning approaches for these robots are inspired by developmental processes and learning mechanisms observed in children. It is widely accepted that developmental robots must autonomously develop, acquire their skills, and cope with unforeseen challenges in unbounded environments through lifelong learning. Continuous online adaptation and intrinsically motivated learning are thus essential capabilities for these robots. However, the high sample-complexity of online learning and intrinsic motivation methods impedes the efficiency and practical feasibility of these methods for lifelong learning. Consequently, the majority of previous work has been demonstrated only in simulation. This thesis devises new methods and learning schemes to mitigate this problem and to permit direct online training on physical robots. A novel intrinsic motivation method is developed to drive the robot’s exploration to efficiently select what to learn. This method combines new knowledge-based and competence-based signals to increase sample-efficiency and to enable lifelong learning. While developmental robots typically acquire their skills through self-exploration, their autonomous development could be accelerated by additionally learning from humans. Yet there is hardly any research to integrate intrinsic motivation with learning from a teacher. The thesis therefore establishes a new learning scheme to integrate intrinsic motivation with learning from observation. The underlying exploration mechanism in the proposed learning schemes relies on Goal Babbling as a goal-directed method for learning direct inverse robot models online, from scratch, and in a learning while behaving fashion. Online learning of multiple solutions for redundant robots with this framework was missing. This thesis devises an incremental online associative network to enable simultaneous exploration and solution consolidation and establishes a new technique to stabilize the learning system. The proposed methods and learning schemes are demonstrated for acquiring reaching skills. Their efficiency, stability, and applicability are benchmarked in simulation and demonstrated on a physical 7-DoF Baxter robot arm.Jüngste Entwicklungen in der Robotik und den Kognitionswissenschaften haben zu einer Generation von vielseitigen Robotern geführt, die als ”Developmental Robots” bezeichnet werden. Lernverfahren für diese Roboter sind inspiriert von Lernmechanismen, die bei Kindern beobachtet wurden. ”Developmental Robots” müssen autonom Fertigkeiten erwerben und unvorhergesehene Herausforderungen in uneingeschränkten Umgebungen durch lebenslanges Lernen meistern. Kontinuierliches Anpassen und Lernen durch intrinsische Motivation sind daher wichtige Eigenschaften. Allerdings schränkt der hohe Aufwand beim Generieren von Datenpunkten die praktische Nutzbarkeit solcher Verfahren ein. Daher wurde ein Großteil nur in Simulationen demonstriert. In dieser Arbeit werden daher neue Methoden konzipiert, um dieses Problem zu meistern und ein direktes Online-Training auf realen Robotern zu ermöglichen. Dazu wird eine neue intrinsisch motivierte Methode entwickelt, die während der Umgebungsexploration effizient auswählt, was gelernt wird. Sie kombiniert neue wissens- und kompetenzbasierte Signale, um die Sampling-Effizienz zu steigern und lebenslanges Lernen zu ermöglichen. Während ”Developmental Robots” Fertigkeiten durch Selbstexploration erwerben, kann ihre Entwicklung durch Lernen durch Beobachten beschleunigt werden. Dennoch gibt es kaum Arbeiten, die intrinsische Motivation mit Lernen von interagierenden Lehrern verbinden. Die vorliegende Arbeit entwickelt ein neues Lernschema, das diese Verbindung schafft. Der in den vorgeschlagenen Lernmethoden genutzte Explorationsmechanismus beruht auf Goal Babbling, einer zielgerichteten Methode zum Lernen inverser Modelle, die online-fähig ist, kein Vorwissen benötigt und Lernen während der Ausführung von Bewegungen ermöglicht. Das Online-Lernen mehrerer Lösungen inverser Modelle redundanter Roboter mit Goal Babbling wurde bisher nicht erforscht. In dieser Arbeit wird dazu ein inkrementell lernendes, assoziatives neuronales Netz entwickelt und eine Methode konzipiert, die es stabilisiert. Das Netz ermöglicht deren gleichzeitige Exploration und Konsolidierung. Die vorgeschlagenen Verfahren werden für das Greifen nach Objekten demonstriert. Ihre Effizienz, Stabilität und Anwendbarkeit werden simulativ verglichen und mit einem Roboter mit sieben Gelenken demonstriert

Essentials of a Theory of Language Cognition

Cognition is not just ‘in the head’; it extends well beyond the skull and the skin. Non‐Cartesian Cognitive Science views cognition as being embodied, environmentally embedded, enacted, encultured, and socially distributed. The Douglas Fir Group (2016) likewise recognizes languages as emergent, social, integrated phenomena. Language is the quintessence of distributed cognition. Language cognition is shared across naturally occurring, culturally constituted, communicative activities. Usage affects learning and it affects languages, too. These are essential components of a theory of language cognition. This article summarizes these developments within cognitive science before considering implications for language research and teaching, especially as these concern usage‐based language learning and cognition in second language and multilingual contexts. Here, I prioritize research involving corpus‐, computational‐, and psycho‐linguistics, and cognitive psychological, complex adaptive system, and network science investigations of learner–language interactions. But there are many other implications. Looking at languages through any one single lens does not do the phenomena justice. Taking the social turn does not entail restricting our research focus to the social. Nor does it obviate more traditional approaches to second language acquisition. Instead it calls for greater transdisciplinarity, diversity, and collaborative work.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147861/1/modl12532_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147861/2/modl12532.pd

Educational Learning Theories: 2nd Edition

This open textbook was the result of a remix of pre-existing open materials collected and reviewed by Molly Zhou and David Brown. Learning theories covered include the theories of Piaget, Bandura, Vygotsky, Kohlberg, Dewey, Bronfenbrenner, Eriksen, Gardner, Bloom, and Maslow. The textbook was revised in 2018 through a Round Ten Revisions and Ancillary Materials Mini-Grant. Topics covered include: Behaviorism Cognitive Development Social Cognitive Theory Experiential Learning Theory Human Motivation Theory Information Processing Theoryhttps://oer.galileo.usg.edu/education-textbooks/1000/thumbnail.jp

Changing My Perspective on Intelligence

This paper originates from a deep desire to understand how historic values of intelligence have led to our modern-day conceptions of intelligence. After only five years of teaching, I was drawn to this topic as I felt it was connected to the service I provide my students and the community in a position as a lead teacher, program coordinator and teacher\u27s aide. The question of the nature of intelligence and aptitude greatly impacts the feedback we offer students, intended for their intellectual growth and academic development. I attempt to distinguish myths from realities about how intelligence evolves and is measured, by exploring the works ranging from those of Alfred Binet and Lewis Terman, who founded intelligence testing, to the Instrumental Enrichment Program of Reuven Feuerstein (FIE), among other, more contemporary analysts and scholars such as Howard Gardner, Daniel Goleman, and Robert Sternberg. I address directly variations in our conceptions of intelligence and their influence on curriculum and teacher practice in the American classroom. My exposure to this topic began in the early part of my graduate career. Through the Critical and Creative Thinking program I have been faced with many challenges, including uprooting old assumptions about what intelligence really is. Inculcated by my family and in school, I believed the IQ test was the absolute measure of whether an individual was smart or not. None of my ideas acknowledged what the true plasticity of the mind was. I had not yet gained an understanding of the necessity for both critical thinking and a creative outlet. My goal in this Synthesis is to speak to fellow teachers, in elementary school and secondary education, to help them consider how outdated conceptions of intelligence still shape our impressions of what processes and knowledge are valuable in our classrooms. In the paper I incorporate alternatives to the mainstream teacher tools through FIE, so teachers can develop professionally and holistically and therein greatly enhance the success of their students. I propose that teachers must first acquire the skills necessary to be able to recognize potential in student work and encourage in them the habits of mind which will develop thoughtful, motivated students

Museum -based learning: Informal learning settings and their role in student motivation and achievement in science

This study examined changes in student motivation and achievement in science in relationship with a visit to the IDEA Place Experiment Gallery. The study was based on the pretest-posttest control comparison group design with four treatment groups: control, exhibit, lesson, and exhibit/lesson. The sample was 228 sixth grade students from a public north central Louisiana school who were randomly assigned to one of the four experimental groups. Pretest, posttest, and delayed posttest measures of intrinsic motivation and achievement in science were determined using the Children\u27s Academic Intrinsic Motivation Inventory and an achievement test written to measure areas of science incorporated in the Experiment Gallery exhibits. The data were analyzed using a one way Analysis of Variance (ANOVA), dependent t tests, and Pearson r. Statistical analysis revealed: (a) no significant differences in motivation or achievement on pretest and posttest scores between groups and, (b) no significant relationships between motivation level and achievement between groups on the posttest. Significant differences were found within groups for (a) the lesson group in motivation, and (b) the exhibit group in achievement from pretest to posttest and from posttest to delayed posttest. A significant relationship between level of motivation and science achievement was revealed for the exhibit group on the delayed posttests. There were no other significant findings to support that the effects of the treatment led to any long term effects on motivation or achievement within any of the four experimental groups