The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multi-robot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks such as monitoring, exploration, and search and rescue operations. This paper presents a deep reinforcement learning-based affective workload allocation controller specifically for multi-human multi-robot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multi-robot systems. The operators' performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we use a multi-human multi-robot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators' cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multi-human multi-robot teams.

翻译：人类与多个机器人之间的交互与协作代表了一个被称为人员-多机器人系统的新兴研究领域。该领域内设计得当的系统使得由人员和机器人组成的团队能够在监控、勘探以及搜救等任务中高效协同工作。本文提出了一种基于深度强化学习的情感工作负荷分配控制器，专门用于多人员-多机器人团队。该控制器能够根据操作员在与多机器人系统协同执行任务期间的表现，动态地重新分配工作负荷。操作员的表现通过自报告问卷评分（即主观测量）以及基于深度学习的认知负荷预测算法结果（该算法使用生理与行为数据，即客观测量）进行评估。为验证所提出控制器的有效性，我们以多人员-多机器人闭路电视监控任务为例，开展了包含32名人类受试者的综合实地实验，进行定量测量与定性分析。我们的结果证明了所提出控制器的性能与有效性，并强调了结合操作员认知负荷的主客观测量以及征得工作负荷转移同意对于提升多人员-多机器人团队绩效的重要性。