  We enable efficient and effective coordination in unpredictable environments,
i.e., in environments whose future evolution is unknown a priori and even
adversarial. We are motivated by the future of autonomy that involves multiple
robots coordinating in dynamic, unstructured, and adversarial environments to
complete complex tasks such as target tracking, environmental mapping, and area
monitoring. Such tasks are often modeled as submodular maximization
coordination problems. We introduce the first submodular coordination algorithm
with bounded tracking regret, i.e., with bounded suboptimality with respect to
optimal time-varying actions that know the future a priori. The bound
gracefully degrades with the environments' capacity to change adversarially. It
also quantifies how often the robots must re-select actions to "learn" to
coordinate as if they knew the future a priori. The algorithm requires the
robots to select actions sequentially based on the actions selected by the
previous robots in the sequence. Particularly, the algorithm generalizes the
seminal Sequential Greedy algorithm by Fisher et al. to unpredictable
environments, leveraging submodularity and algorithms for the problem of
tracking the best expert. We validate our algorithm in simulated scenarios of
target tracking.
