Recent years have seen falling costs of communication and storage technologies and advances in fabrication methods. Sensors, actuators, and processors are being integrated into globally accessible information networks. These trends are promoting a profusion of networked robotic platforms with distinct features and unique capabilities. As we aspire to harness this diverse array of robots to solve increasingly complex problems, heterogeneity and diversity become design features. However, we still lack a fundamental understanding of how to compose and control large-scale systems of heterogeneous robots. Moreover, as we program diverse robots to exploit their technical complementarities, we create interdependencies and critical links.  Such collaborative algorithms give rise to new sources of internal and external threats that lead to unintended failure modes. As a consequence, we need new mechanisms that ensure resilience.

I begin my talk by formalizing diversity in the context of dynamic task allocation for large-scale heterogeneous multi-robot systems. In light of this setting, I show how optimal control policies are impacted by the heterogeneity of the robot team. In the second part of the talk, my focus shifts to the question of how to provide resilience to internal failures through precautionary collaboration mechanisms. By building on foundational concepts of network science and security, I show how we can achieve resilience, allowing robot teams to function in the presence of defective and/or malicious robots. Finally, I consider the importance of providing system-wide protection against external threats, and introduce some new ideas that touch upon privacy.