  We consider the coordinated escort problem, where a decentralised team of
supporting robots implicitly assist the mission of higher-value principal
robots. The defining challenge is how to evaluate the effect of supporting
robots' actions on the principal robots' mission. To capture this effect, we
define two novel auxiliary reward functions for supporting robots called
satisfaction improvement and satisfaction entropy, which computes the
improvement in probability of mission success, or the uncertainty thereof.
Given these reward functions, we coordinate the entire team of principal and
supporting robots using decentralised cross entropy method (Dec-CEM), a new
extension of CEM to multi-agent systems based on the product distribution
approximation. In a simulated object avoidance scenario, our planning framework
demonstrates up to two-fold improvement in task satisfaction against
conventional decoupled information gathering.The significance of our results is
to introduce a new family of algorithmic problems that will enable important
new practical applications of heterogeneous multi-robot systems.
