  Understanding collective behavior and how it evolves is important to ensure
that robot swarms can be trusted in a shared environment. One way to understand
the behavior of the swarm is through collective behavior reconstruction using
prior demonstrations. Existing approaches often require access to the swarm
controller which may not be available. We reconstruct collective behaviors in
distinct swarm scenarios involving shared environments without using swarm
controller information. We achieve this by transforming prior demonstrations
into features that describe multi-agent interactions before behavior
reconstruction with multi-agent generative adversarial imitation learning
(MA-GAIL). We show that our approach outperforms existing algorithms in spatial
organization, and can be used to observe and reconstruct a swarm's behavior for
further analysis and testing, which might be impractical or undesirable on the
original robot swarm.
