  Recent advances in multi-agent reinforcement learning (MARL) are enabling
impressive coordination in heterogeneous multi-robot teams. However, existing
approaches often overlook the challenge of generalizing learned policies to
teams of new compositions, sizes, and robots. While such generalization might
not be important in teams of virtual agents that can retrain policies
on-demand, it is pivotal in multi-robot systems that are deployed in the
real-world and must readily adapt to inevitable changes. As such, multi-robot
policies must remain robust to team changes -- an ability we call adaptive
teaming. In this work, we investigate if awareness and communication of robot
capabilities can provide such generalization by conducting detailed experiments
involving an established multi-robot test bed. We demonstrate that shared
decentralized policies, that enable robots to be both aware of and communicate
their capabilities, can achieve adaptive teaming by implicitly capturing the
fundamental relationship between collective capabilities and effective
coordination. Videos of trained policies can be viewed at:
https://sites.google.com/view/cap-comm
