Initial Task Assignment in Multi-Human Multi-Robot Teams: An Attention-enhanced Hierarchical Reinforcement Learning Approach

TL;DR

This paper introduces an attention-enhanced hierarchical reinforcement learning method for initial task assignment in large-scale multi-human multi-robot teams, improving decision-making efficiency for complex, long-horizon missions.

Contribution

It proposes a novel hierarchical RL framework with a cross-attribute attention mechanism to better decompose and solve complex initial task assignment problems.

Findings

Enhanced task allocation efficiency demonstrated in case study

Hierarchical attention improves sub-policy learning

Effective handling of large-scale heterogeneous teams

Abstract

Multi-human multi-robot teams (MH-MR) obtain tremendous potential in tackling intricate and massive missions by merging distinct strengths and expertise of individual members. The inherent heterogeneity of these teams necessitates advanced initial task assignment (ITA) methods that align tasks with the intrinsic capabilities of team members from the outset. While existing reinforcement learning approaches show encouraging results, they might fall short in addressing the nuances of long-horizon ITA problems, particularly in settings with large-scale MH-MR teams or multifaceted tasks. To bridge this gap, we propose an attention-enhanced hierarchical reinforcement learning approach that decomposes the complex ITA problem into structured sub-problems, facilitating more efficient allocations. To bolster sub-policy learning, we introduce a hierarchical cross-attribute attention (HCA) mechanism, encouraging each sub-policy within the hierarchy to discern and leverage the specific nuances in the state space that are crucial for its respective decision-making phase. Through an extensive environmental surveillance case study, we demonstrate the benefits of our model and the HCA inside.