HULK: Large-scale Hierarchical Coordination under Continual and Uncertain Temporal Tasks

2026-05-09

AI Summary

This work addresses the online coordination challenges faced by large-scale multi-agent systems when handling continuously generated, temporally constrained tasks with uncertain quantities. The authors propose a hierarchical coordination framework in which a high-level planner employs receding-horizon optimization to allocate known tasks to subteams, while a low-level coordinator dynamically orchestrates agents within each subteam to respond to newly detected subtasks in real time. Innovatively integrating hierarchical coordination with temporal logic task specifications, the approach enables efficient and robust collaboration across multiple granularities and triggering conditions, thereby avoiding costly global recomputation and excessive communication overhead. Experimental results demonstrate that the method achieves superior efficiency, scalability, and robustness in large-scale heterogeneous multi-agent systems under various environmental uncertainties.

Abstract

Multi-agent systems can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. Coordination of such teams often involves two aspects: selecting appropriate subteams for different tasks in various areas, and coordinating agents in the subteams to execute the associated subtasks. Existing work often assumes that the tasks are static and known beforehand, where an integer program can be formulated and solved offline. However, in many applications, the team-wise tasks are generated online continually by external requests, and the amount of subtasks within each task is uncertain, e.g., the number of packages to deliver or victims to rescue. The aforementioned offline solution becomes inadequate as it would require constant re-computation for the whole team and global communication to broadcast the results. Thus, this work tackles the large-scale coordination problem under continual and uncertain temporal tasks, specified as temporal logic formulas over collaborative actions. The proposed hierarchical framework, HULK, consists of two interleaved layers: the rolling assignment of currently known tasks to subteams within a certain horizon, and the dynamic coordination within a subteam given the detected subtasks during online execution. Thus, coordination is performed hierarchically at different granularities and triggering conditions, improving computational efficiency and robustness. The method is validated rigorously over large-scale heterogeneous systems under various temporal tasks and environment uncertainties.

Problem

• multi-agent coordination
• continual tasks
• uncertain temporal tasks
• large-scale systems
• online task generation

Innovation

• hierarchical coordination
• continual tasks
• uncertain subtasks
• temporal logic
• multi-agent systems