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.

翻译：我们实现了在不可预测环境中的高效且有效的协调，即环境的未来演化未知且甚至具有对抗性。这一研究受到未来自主性的启发，其中涉及多机器人在动态、非结构化和对抗性环境中协调完成复杂任务，如目标追踪、环境制图和区域监测。此类任务通常被建模为子模最大化协调问题。我们首次提出具有有界追踪遗憾的子模协调算法，即相对于已知未来先验的最优时变动作具有有界次优性。该界限随环境对抗性变化的能力优雅地退化，并量化了机器人必须重新选择动作以“学习”协调的频率，仿佛它们先验地知晓未来。算法要求机器人基于序列中先前机器人选择的动作顺序选择动作。特别地，该算法将Fisher等人提出的经典序列贪婪算法推广至不可预测环境，利用子模性和追踪最优专家问题算法。我们在目标追踪的模拟场景中验证了所提算法。