# Deep learning control of artificial avatars in group coordination tasks

## TL;DR

This paper presents a deep reinforcement learning-based control architecture for artificial agents to achieve human-like coordination within groups during joint tasks, addressing a less explored multi-agent scenario.

## Contribution

It introduces a novel deep learning control method enabling artificial agents to synchronize with human groups in multi-agent coordination tasks.

## Key findings

- Successful synthesis of artificial agents that coordinate with human groups
- Agents exhibit human-like kinematic features during group tasks
- Demonstrated effectiveness on the mirror-game benchmark

## Abstract

In many joint-action scenarios, humans and robots have to coordinate their movements to accomplish a given shared task. Lifting an object together, sawing a wood log, transferring objects from a point to another are all examples where motor coordination between humans and machines is a crucial requirement. While the 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 able to coordinate its motion in human ensembles. Driven by a control architecture based on deep reinforcement learning, such an artificial agent will be able to autonomously move itself in order to synchronise its motion with that of the group while exhibiting human-like kinematic features. As a paradigmatic coordination task we take a group version of the so-called mirror-game which is highlighted as a good benchmark in the human movement literature.