We enable efficient and effective coordination in unpredictable environments, ie., 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, image covering, and area monitoring. Such tasks are often modeled as submodular maximization coordination problems. We thus introduce the first submodular coordination algorithm with bounded tracking regret, ie., 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. Our 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.

在未知和对抗性的动态和非结构化环境中, 提出一种带边界的次模协调算法，使得机器人可以在协调完成复杂任务的同时，学习到在最优时间内采取最优行动的能力。

具有有界追踪遗憾的在线子模合作：理论、算法和多机器人协调应用