  Leader-follower navigation is a popular class of multi-robot algorithms where
a leader robot leads the follower robots in a team. The leader has specialized
capabilities or mission-critical information (e.g. goal location) that the
followers lack, and this makes the leader crucial for the mission's success.
However, this also makes the leader a vulnerability - an external adversary who
wishes to sabotage the robot team's mission can simply harm the leader and the
whole robot team's mission would be compromised. Since robot motion generated
by traditional leader-follower navigation algorithms can reveal the identity of
the leader, we propose a defense mechanism of hiding the leader's identity by
ensuring the leader moves in a way that behaviorally camouflages it with the
followers, making it difficult for an adversary to identify the leader. To
achieve this, we combine Multi-Agent Reinforcement Learning, Graph Neural
Networks and adversarial training. Our approach enables the multi-robot team to
optimize the primary task performance with leader motion similar to follower
motion, behaviorally camouflaging it with the followers. Our algorithm
outperforms existing work that tries to hide the leader's identity in a
multi-robot team by tuning traditional leader-follower control parameters with
Classical Genetic Algorithms. We also evaluated human performance in inferring
the leader's identity and found that humans had lower accuracy when the robot
team used our proposed navigation algorithm.
