  Multi-robot systems of increasing size and complexity are used to solve
large-scale problems, such as area exploration and search and rescue. A key
decision in human-robot teaming is dividing a multi-robot system into teams to
address separate issues or to accomplish a task over a large area. In order to
address the problem of selecting teams in a multi-robot system, we propose a
new multimodal graph embedding method to construct a unified representation
that fuses multiple information modalities to describe and divide a multi-robot
system. The relationship modalities are encoded as directed graphs that can
encode asymmetrical relationships, which are embedded into a unified
representation for each robot. Then, the constructed multimodal representation
is used to determine teams based upon unsupervised learning. We perform
experiments to evaluate our approach on expert-defined team formations,
large-scale simulated multi-robot systems, and a system of physical robots.
Experimental results show that our method successfully decides correct teams
based on the multifaceted internal structures describing multi-robot systems,
and outperforms baseline methods based upon only one mode of information, as
well as other graph embedding-based division methods.
