Multi-human multi-robot teams have great potential for complex and large-scale tasks through the collaboration of humans and robots with diverse capabilities and expertise. To efficiently operate such highly heterogeneous teams and maximize team performance timely, sophisticated initial task allocation strategies that consider individual differences across team members and tasks are required. While existing works have shown promising results in reallocating tasks based on agent state and performance, the neglect of the inherent heterogeneity of the team hinders their effectiveness in realistic scenarios. In this paper, we present a novel formulation of the initial task allocation problem in multi-human multi-robot teams as contextual multi-attribute decision-make process and propose an attention-based deep reinforcement learning approach. We introduce a cross-attribute attention module to encode the latent and complex dependencies of multiple attributes in the state representation. We conduct a case study in a massive threat surveillance scenario and demonstrate the strengths of our model.

翻译：多人类多机器人团队通过具备多样能力与专业知识的人类与机器人协作，在复杂大规模任务中展现出巨大潜力。为高效运作这种高度异质性的团队并最大化团队性能的时效性，需要制定考虑团队成员与任务间个体差异的精密初始任务分配策略。现有研究虽已基于智能体状态与性能在任务重分配方面取得显著进展，但忽视团队固有异质性制约了其在真实场景中的有效性。本文提出将多人类多机器人团队初始任务分配问题形式化为背景多属性决策过程，并设计了一种基于注意力机制的深度强化学习方法。我们引入跨属性注意力模块，在状态表征中编码多个属性间潜在且复杂的依赖关系。通过大规模威胁监视场景的案例研究，验证了该模型的有效性。