Enaction as a Conceptual Framework for Developmental Cognitive Robotics

AbstractThis paper provides an accessible introduction to the cognitive systems paradigm of enaction and shows how it forms a practical framework for robotic systems that can develop cognitive abilities. The principal idea of enaction is that a cognitive system develops it own understanding of the world around it through its interactions with the environment. Thus, enaction entails that the cognitive system operates autonomously and that it generates its own models of how the world works. A discussion of the five key elements of enaction — autonomy, embodiment, emergence, experience, and sense-making — leads to a core set of functional, organizational, and developmental requirements which are then used in the design of a cognitive architecture for the iCub humanoid robot

An Open-Source Simulator for Cognitive Robotics Research: The Prototype of the iCub Humanoid Robot Simulator

This paper presents the prototype of a new computer simulator for the humanoid robot iCub. The iCub is a new open-source humanoid robot developed as a result of the “RobotCub” project, a collaborative European project aiming at developing a new open-source cognitive robotics platform. The iCub simulator has been developed as part of a joint effort with the European project “ITALK” on the integration and transfer of action and language knowledge in cognitive robots. This is available open-source to all researchers interested in cognitive robotics experiments with the iCub humanoid platform

Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition, 10-12 September 2012, Lausanne, Switzerland

The aim of the Postgraduate Conference on Robotics and Development of Cognition (RobotDoC-PhD) is to bring together young scientists working on developmental cognitive robotics and its core disciplines. The conference aims to provide both feedback and greater visibility to their research as lively and stimulating discussion can be held amongst participating PhD students and senior researchers. The conference is open to all PhD students and post-doctoral researchers in the field. RobotDoC-PhD conference is an initiative as a part of Marie-Curie Actions ITN RobotDoC and will be organized as a satellite event of the 22nd International Conference on Artificial Neural Networks ICANN 2012

Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition, 10-12 September 2012, Lausanne, Switzerland

The aim of the Postgraduate Conference on Robotics and Development of Cognition (RobotDoC-PhD) is to bring together young scientists working on developmental cognitive robotics and its core disciplines. The conference aims to provide both feedback and greater visibility to their research as lively and stimulating discussion can be held amongst participating PhD students and senior researchers. The conference is open to all PhD students and post-doctoral researchers in the field. RobotDoC-PhD conference is an initiative as a part of Marie-Curie Actions ITN RobotDoC and will be organized as a satellite event of the 22nd International Conference on Artificial Neural Networks ICANN 2012

Modelling Learning to Count in Humanoid Robots

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Plymouth University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.This thesis concerns the formulation of novel developmental robotics models of embodied phenomena in number learning. Learning to count is believed to be of paramount importance for the acquisition of the remarkable fluency with which humans are able to manipulate numbers and other abstract concepts derived from them later in life. The ever-increasing amount of evidence for the embodied nature of human mathematical thinking suggests that the investigation of numerical cognition with the use of robotic cognitive models has a high potential of contributing toward the better understanding of the involved mechanisms. This thesis focuses on two particular groups of embodied effects tightly linked with learning to count. The first considered phenomenon is the contribution of the counting gestures to the counting accuracy of young children during the period of their acquisition of the skill. The second phenomenon, which arises over a longer time scale, is the human tendency to internally associate numbers with space that results, among others, in the widely-studied SNARC effect. The PhD research contributes to the knowledge in the subject by formulating novel neuro-robotic cognitive models of these phenomena, and by employing these in two series of simulation experiments. In the context of the counting gestures the simulations provide evidence for the importance of learning the number words prior to learning to count, for the usefulness of the proprioceptive information connected with gestures to improving counting accuracy, and for the significance of the spatial correspondence between the indicative acts and the objects being enumerated. In the context of the model of spatial-numerical associations the simulations demonstrate for the first time that these may arise as a consequence of the consistent spatial biases present when children are learning to count. Finally, based on the experience gathered throughout both modelling experiments, specific guidelines concerning future efforts in the application of robotic modelling in mathematical cognition are formulated.This research has been supported by the EU project RobotDoC (235065) from the FP7 Marie Curie Actions ITN

Interaction Histories and Short-Term Memory: Enactive Development of Turn-Taking Behaviours in a Childlike Humanoid Robot

In this article, an enactive architecture is described that allows a humanoid robot to learn to compose simple actions into turn-taking behaviours while playing interaction games with a human partner. The robot’s action choices are reinforced by social feedback from the human in the form of visual attention and measures of behavioural synchronisation. We demonstrate that the system can acquire and switch between behaviours learned through interaction based on social feedback from the human partner. The role of reinforcement based on a short-term memory of the interaction was experimentally investigated. Results indicate that feedback based only on the immediate experience was insufficient to learn longer, more complex turn-taking behaviours. Therefore, some history of the interaction must be considered in the acquisition of turn-taking, which can be efficiently handled through the use of short-term memory.Peer reviewedFinal Published versio

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

From Penguins to Parakeets: a Developmental Approach to Modelling Conceptual Prototypes

The use of concepts is a fundamental capacity underlying complex, human-level cognition. A number of theories have explored the means of concept representation and their links to lower-level features, with one notable example being the Conceptual Spaces theory. While these provide an account for such essential functional processes as prototypes and typicality, it is not entirely clear how these aspects of human cognition can arise in a system undergoing continuous development - postulated to be a necessity from the developmental systems perspective. This paper seeks to establish the foundation of an approach to this question by showing that a distributed, associative and continuous development mechanism, founded on principles of biological memory, can achieve classification performance comparable to the Conceptual Spaces model. We show how qualitatively similar prototypes are formed by both systems when exposed to the same dataset, which illustrates how both models can account for the development of conceptual primitives