A review of abstract concept learning in embodied agents and robots.

This paper reviews computational modelling approaches to the learning of abstract concepts and words in embodied agents such as humanoid robots. This will include a discussion of the learning of abstract words such as 'use' and 'make' in humanoid robot experiments, and the acquisition of numerical concepts via gesture and finger counting strategies. The current approaches share a strong emphasis on embodied cognition aspects for the grounding of abstract concepts, and a continuum, rather than dichotomy, view of concrete/abstract concepts differences.This article is part of the theme issue 'Varieties of abstract concepts: development, use and representation in the brain'

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

Making sense of words: a robotic model for language abstraction

Building robots capable of acting independently in unstructured environments is still a challenging task for roboticists. The capability to comprehend and produce language in a 'human-like' manner represents a powerful tool for the autonomous interaction of robots with human beings, for better understanding situations and exchanging information during the execution of tasks that require cooperation. In this work, we present a robotic model for grounding abstract action words (i.e. USE, MAKE) through the hierarchical organization of terms directly linked to perceptual and motor skills of a humanoid robot. Experimental results have shown that the robot, in response to linguistic commands, is capable of performing the appropriate behaviors on objects. Results obtained in case of inconsistency between the perceptual and linguistic inputs have shown that the robot executes the actions elicited by the seen object

A motivational approach to support healthy habits in long-term child–robot interaction

We examine the use of role-switching as an intrinsic motivational mechanism to increase engagement in long-term child–robot interaction. The present study describes a learning framework where children between 9 and 11-years-old interact with a robot to improve their knowledge and habits with regards to healthy life-styles. Experiments were carried out in Italy where 41 children were divided in three groups interacting with: (i) a robot with a role-switching mechanism, (ii) a robot without a role-switching mechanism and (iii) an interactive video. Additionally, a control group composed of 43 more children, who were not exposed to any interactive approach, was used as a baseline of the study. During the intervention period, the three groups were exposed to three interactive sessions once a week. The aim of the study was to find any difference in healthy-habits acquisition based on alternative interactive systems, and to evaluate the effectiveness of the role-switch approach as a trigger for engagement and motivation while interacting with a robot. The results provide evidence that the rate of children adopting healthy habits during the intervention period was higher for those interacting with a robot. Moreover, alignment with the robot behaviour and achievement of higher engagement levels were also observed for those children interacting with the robot that used the role-switching mechanism. This supports the notion that role-switching facilitates sustained long-interactions between a child and a robot

Developmental Bootstrapping of AIs

Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure

Conversational Agents in Education – A Systematic Literature Review

Conversational Agents (CAs) are widely spread in a variety of domains, such as health and customer service. There is a recent trend of increasing publications and implementations of CAs in education. We conduct a systematic literature review to identify common methodologies, pedagogical CA roles, addressed target groups, the technologies and theories behind, as well as human-like design aspects. The initially found 3329 records were systematically reduced to 252 fully coded articles. Based on the analysis of the codings, we derive further research streams. Our results reveal a research gap for long-term studies on the use of CAs in education, and there is insufficient holistic design knowledge for pedagogical CAs. Moreover, target groups other than academic students are rarely considered. We condense our findings in a morphological box and conclude that pedagogical CAs have not yet reached their full potential of long-term practical application in education

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book