Multimodal Child-Robot Interaction: Building Social Bonds

Tony Belpaeme, Paul E Baxter, Robin Read, Rachel Wood, Heriberto Cuayáhuitl, Bernd Kiefer, Stefania Racioppa, Ivana Kruijff-Korbayová, Georgios Athanasopoulos, Valentin Enescu, Rosemarijn Looije, Mark Neerincx, Yiannis Demiris, Raquel Ros-Espinoza, Aryel Beck, Lola Cañamero, Antione Hiolle, Matthew Lewis, Ilaria Baroni, Marco Nalin, Piero Cosi, Giulio Paci, Fabio Tesser, Giacomo Sommavilla, Remi Humbert

Abstract


For robots to interact effectively with human users they must be capable of coordinated, timely behavior in response to social context. The Adaptive Strategies for Sustainable Long-Term Social Interaction (ALIZ-E) project focuses on the design of long-term, adaptive social interaction between robots and child users in real-world settings. In this paper, we report on the iterative approach taken to scientific and technical developments toward this goal: advancing individual technical competen- cies and integrating them to form an autonomous robotic system for evaluation “in the wild.” The first evaluation iterations have shown the potential of this methodology in terms of adaptation of the robot to the interactant and the resulting influences on engagement. This sets the foundation for an ongoing research program that seeks to develop technologies for social robot companions.

Keywords


child-robot interaction, robot assisted therapy, large-scale project, long-term HRI, natural language interaction, integration for HRI.

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