Deep learning control of artificial avatars in group coordination tasks

Abstract

In many joint-action scenarios, humans and robots have to coordinate their movements to accomplish a given shared task. Examples include lifting an object together, sawing a wood log, transferring objects from a point to another. While dyadic coordination between a human and a robot has been studied in previous investigations, the multi-agent scenario in which a robot has to be integrated into a human group still remains a less explored field of research. In this paper we discuss how to synthesise an artificial agent, driven by a control architecture based on deep reinforcement learning, able to coordinate its motion in human ensembles. As a paradigmatic coordination task we take a group version of the so called mirror game from the human movement literature.