  We present a technique for learning how to solve a multi-robot mission that
requires interaction with an external environment by observing an expert system
executing the same mission. We define the expert system as a team of robots
equipped with a library of controllers, each designed to solve a specific task,
supervised by an expert policy that appropriately selects controllers based on
the states of robots and environment. The objective is for an un-trained team
of robots (i.e., imitator system) equipped with the same library of
controllers, but agnostic to the expert policy, to execute the mission, with
performances comparable to those of the expert system. From un-annotated
observations of the expert system, a multi-hypothesis filtering technique is
used to estimate individual controllers executed by the expert policy. Then,
the history of estimated controllers and environmental states is used to train
a neural network policy for the imitator system. Considering a perimeter
protection scenario on a team of differential-drive robots, we show that the
learned policy endows the imitator system with performances comparable to those
of the expert system.
