Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

Integrating Socially Assistive Robots into Language Tutoring Systems. A Computational Model for Scaffolding Young Children's Foreign Language Learning

Schodde T. Integrating Socially Assistive Robots into Language Tutoring Systems. A Computational Model for Scaffolding Young Children's Foreign Language Learning. Bielefeld: Universität Bielefeld; 2019.Language education is a global and important issue nowadays, especially for young children since their later educational success build on it. But learning a language is a complex task that is known to work best in a social interaction and, thus, personalized sessions tailored to the individual knowledge and needs of each child are needed to allow for teachers to optimally support them. However, this is often costly regarding time and personnel resources, which is one reasons why research of the past decades investigated the benefits of Intelligent Tutoring Systems (ITSs). But although ITSs can help out to provide individualized one-on-one tutoring interactions, they often lack of social support. This dissertation provides new insights on how a Socially Assistive Robot (SAR) can be employed as a part of an ITS, building a so-called "Socially Assistive Robot Tutoring System" (SARTS), to provide social support as well as to personalize and scaffold foreign language learning for young children in the age of 4-6 years. As basis for the SARTS a novel approach called A-BKT is presented, which allows to autonomously adapt the tutoring interaction to the children's individual knowledge and needs. The corresponding evaluation studies show that the A-BKT model can significantly increase student's learning gains and maintain a higher engagement during the tutoring interaction. This is partly due to the models ability to simulate the influences of potential actions on all dimensions of the learning interaction, i.e., the children's learning progress (cognitive learning), affective state, engagement (affective learning) and believed knowledge acquisition (perceived learning). This is particularly important since all dimensions are strongly interconnected and influence each other, for example, a low engagement can cause bad learning results although the learner is already quite proficient. However, this also yields the necessity to not only focus on the learner's cognitive learning but to equally support all dimensions with appropriate scaffolding actions. Therefore an extensive literature review, observational video recordings and expert interviews were conducted to find appropriate actions applicable for a SARTS to support each learning dimension. The subsequent evaluation study confirms that the developed scaffolding techniques are able to support young children’s learning process either by re-engaging them or by providing transparency to support their perception of the learning process and to reduce uncertainty. Finally, based on educated guesses derived from the previous studies, all identified strategies are integrated into the A-BKT model. The resulting model called ProTM is evaluated by simulating different learner types, which highlight its ability to autonomously adapt the tutoring interactions based on the learner's answers and provided dis-engagement cues. Summarized, this dissertation yields new insights into the field of SARTS to provide personalized foreign language learning interactions for young children, while also rising new important questions to be studied in the future

Staying engaged in child-robot interaction:A quantitative approach to studying preschoolers’ engagement with robots and tasks during second-language tutoring

Inleiding Covid-19 heeft laten zien dat onze traditionele manier van lesgeven steeds meer afhankelijk is van digitale hulpmiddelen. In de afgelopen jaren (2020-2021) hebben leerkrachten kinderen online les moeten geven en hebben ouders hun kinderen moeten begeleiden bij hun lesactiviteiten. Digitale instrumenten die het onderwijs kunnen ondersteunen zoals sociale robots, zouden uiterst nuttig zijn geweest voor leerkrachten. Robots die, in tegenstelling tot tablets, hun lichaam kunnen gebruiken om zich vergelijkbaar te gedragen als leerkrachten. Bijvoorbeeld door te gebaren tijdens het praten, waardoor kinderen zich beter kunnen concentreren wat een voordeel oplevert voor hun leerprestaties. Bovendien stellen robots, meer dan tablets, kinderen in staat tot een sociale interactie, wat vooral belangrijk is bij het leren van een tweede taal (L2). Hierover ging mijn promotietraject wat onderdeel was van het Horizon 2020 L2TOR project1, waarin zes verschillende universiteiten en twee bedrijven samenwerkten en onderzochten of een robot aan kleuters woorden uit een tweede taal kon leren. Een van de belangrijkste vragen in dit project was hoe we gedrag van de robot konden ontwikkelen dat kinderen betrokken (engaged) houdt. Betrokkenheid van kinderen is belangrijk zodat zij tijdens langere tijdsperiodes met de robot aan de slag willen. Om deze vraag te beantwoorden, heb ik meerdere studies uitgevoerd om het effect van de robot op de betrokkenheid van kinderen met de robot te onderzoeken, alsmede onderzoek te doen naar de perceptie die de kinderen van de robot hadden. 1Het L2TOR project leverde een grote bijdrage binnen het mens-robot interactie veld in de beweging richting publieke wetenschap. Alle L2TOR publicaties, de project deliverables, broncode en data zijn openbaar gemaakt via de website www.l2tor.eu en via www.github.nl/l2tor en de meeste studies werden vooraf geregistreerd

The EASEL project: Towards educational human-robot symbiotic interaction

This paper presents the EU EASEL project, which explores the potential impact and relevance of a robot in educational settings. We present the project objectives and the theorectical background on which the project builds, briefly introduce the EASEL technological developments, and end with a summary of what we have learned from the evaluation studies carried out in the project so far

Emotion and memory model for social robots: a reinforcement learning based behaviour selection

In this paper, we propose a reinforcement learning (RL) mechanism for social robots to select an action based on users’ learning performance and social engagement. We applied this behavior selection mechanism to extend the emotion and memory model, which allows a robot to create a memory account of the user’s emotional events and adapt its behavior based on the developed memory. We evaluated the model in a vocabulary-learning task at a school during a children’s game involving robot interaction to see if the model results in maintaining engagement and improving vocabulary learning across the four different interaction sessions. Generally, we observed positive findings based on child vocabulary learning and sustaining social engagement during all sessions. Compared to the trends of a previous study, we observed a higher level of social engagement across sessions in terms of the duration of the user gaze toward the robot. For vocabulary retention, we saw similar trends in general but also showing high vocabulary retention across some sessions. The findings indicate the benefits of applying RL techniques that have a reward system based on multi-modal user signals or cues