A software framework for the implementation of dynamic neural field control architectures for human-robot interaction

Abstract

Useful and efficient human-robot interaction in joint tasks requires the design of a cognitive control architecture that endows robots with crucial cognitive and social capabilities such as intention recognition and complementary action selection. Herein, we present a software framework that eases the design and implementation of Dynamic Neural Field (DNF) cognitive architectures for human-robot joint tasks. We provide a graphical user interface to draw instances of the robot's control architecture. In addition, it allows to simulate, inspect and parametrize them in real-time. The framework eases parameter tuning by allowing changes on-the-fly and by connecting the cognitive architecture with simulated or real robots. Using the case study of an anthropomorphic robot providing assistance to a disabled person during a meal scenario, we illustrate the applicability of the framework.The work was funded by Project NETT: Neural Engineering Transformative Technologies, EU-FP7 ITN (nr.289146), and by FCT - Fundação para a Ciência e Tecnologia, through the Phd and Posdoc Grants (SFRH/BD/81334/2011 and SFRH/BPD/71874/2010 respectively, financed by POPH-QREN-Type 4.1- Advanced Training, co-funded by the European Social Fund and national funds from MEC), and Project Scope: UID/CEC/00319/2013 together with COMPETE: POCI-01-0145-FEDER007043.info:eu-repo/semantics/publishedVersio