[Supplementary Video] Initial Workload Allocation for Multi-Human Multi-Robot Teams

 

This is a supplementary video for the paper, titled "Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning", in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), by Ruiqi Wang, Dezhong Zhao, and Byung-Cheol Min Paper (ArXiv) Link: https://arxiv.org/abs/2303.02486 Abstract: 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.