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有多样能力和专长的人类与机器人协作，在复杂和大规模任务中展现出巨大潜力。为了高效运作这种高度异质性的团队并及时最大化团队性能，需要制定考虑团队成员与任务间个体差异的精细初始任务分配策略。尽管现有研究在基于智能体状态和性能进行任务重分配方面取得了良好成果，但对团队固有异质性的忽视阻碍了其在现实场景中的有效性。本文提出了一种新的多人类多机器人团队初始任务分配问题建模方法，将其视为情境化多属性决策过程，并引入基于注意力的深度强化学习方案。我们设计了一个跨属性注意力模块，用于编码状态表示中多个属性的潜在复杂依赖关系。通过大规模威胁监控场景的案例研究，验证了本模型的优势。