Real-world robots often operate in settings where objective priorities depend on the underlying context of operation. When the underlying context is unknown apriori, multiple robots may have to coordinate to gather informative observations to infer the context, since acting based on an incorrect context can lead to misaligned and unsafe behavior. Once the underlying true context is inferred, the robots optimize their task-specific objectives in the preference order induced by the context. We formalize this problem as a Multi-Robot Context-Uncertain Stochastic Shortest Path (MR-CUSSP), which captures context-relevant information at landmark states through joint observations. Our two-stage solution approach is composed of: (1) CIMOP (Coordinated Inference for Multi-Objective Planning) to compute plans that guide robots toward informative landmarks to efficiently infer the true context, and (2) LCBS (Lexicographic Conflict-Based Search) for collision-free multi-robot path planning with lexicographic objective preferences, induced by the context. We evaluate the algorithms using three simulated domains and demonstrate its practical applicability using five mobile robots in the salp domain setup.

翻译：现实世界中的机器人往往在目标优先级取决于运行场景的上下文中工作。当底层上下文事先未知时，多个机器人需要协调以收集信息性观测来推断上下文，因为基于错误上下文行动可能导致行为失准和不安全。一旦推断出真实的底层上下文，机器人会根据上下文引发的偏好顺序优化其任务特定目标。我们将此问题形式化为“多机器人上下文不确定随机最短路径”（MR-CUSSP），通过联合观测捕捉地标状态中的上下文相关信息。我们的两阶段求解方法包括：（1）CIMOP（面向多目标规划的协调推理），用于计算引导机器人前往信息性地标的计划，以高效推断真实上下文；（2）LCBS（词典序冲突搜索），用于在上下文引发的词典序目标偏好下进行无碰撞的多机器人路径规划。我们使用三个模拟域评估了算法，并通过琼脂海域设置中的五个移动机器人展示了其实用适用性。