Learning-based cognitive architecture for enhancing coordination in human groups

TL;DR

This paper introduces a reinforcement-learning-based cognitive architecture that improves synchronization in human-avatar groups, validated through simulations and preliminary real-world experiments, enhancing group coordination in rehabilitation and sports.

Contribution

It presents a novel sim-to-real reinforcement learning framework for cognitive architectures that enhance human-avatar group coordination, with demonstrated effectiveness in synchronization tasks.

Findings

Improved synchronization in simulated environments.

Avatars seamlessly integrate into human groups.

Preliminary real-world experiments show indistinguishability from humans.

Abstract

As interactions with autonomous agents-ranging from robots in physical settings to avatars in virtual and augmented realities-become more prevalent, developing advanced cognitive architectures is critical for enhancing the dynamics of human-avatar groups. This paper presents a reinforcement-learning-based cognitive architecture, trained via a sim-to-real approach, designed to improve synchronization in periodic motor tasks, crucial for applications in group rehabilitation and sports training. Extensive numerical validation consistently demonstrates improvements in synchronization. Theoretical derivations and numerical investigations are complemented by preliminary experiments with real participants, showing that our avatars can integrate seamlessly into human groups, often being indistinguishable from humans.