Adaptive Task Allocation in Multi-Human Multi-Robot Teams under Team Heterogeneity and Dynamic Information Uncertainty

TL;DR

This paper introduces ATA-HRL, an adaptive framework using hierarchical reinforcement learning for task allocation in multi-human multi-robot teams, addressing heterogeneity, dynamic states, and information uncertainty to improve performance.

Contribution

The paper presents a novel hierarchical reinforcement learning framework that adaptively allocates tasks considering team heterogeneity and operational uncertainties.

Findings

Improved task allocation efficiency in large-scale environmental monitoring.

Effective handling of team heterogeneity and dynamic operational states.

Enhanced management of information uncertainty through auxiliary learning.

Abstract

Task allocation in multi-human multi-robot (MH-MR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches often fail to address these challenges simultaneously, resulting in suboptimal performance. To tackle this, we propose ATA-HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic operational states. Additionally, we introduce an auxiliary state representation learning task to manage information uncertainty and enhance task execution. Through an extensive case study in large-scale environmental monitoring tasks, we demonstrate the benefits of our approach.