面向去中心化多人类多机器人任务分配的通感异构图表学习
Communication-Aware Heterogeneous Graph Learning for Decentralized Multi-Human Multi-Robot Task Allocation
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中文总结 AI 辅助
提出CommHG框架，通过通感异构图和协作多智能体强化学习联合优化去中心化多人类多机器人任务分配与通信，实验显示在多达16机器人、6人类、112任务规模下优于最强基线。
中文摘要 AI 辅助
多人类多机器人（MH-MR）团队将机器人自主性与人类专业知识相结合，但有效的任务分配需要协调稀缺且动态可用的人力支持与分布式机器人执行。机器人间及人机间有限的通信通过延迟信息交换和监督干预进一步加剧了这种耦合。我们提出了CommHG，一种用于去中心化MH-MR任务分配的通感异构图表学习框架。CommHG通过基于信息可用性和时效性的局部图来表示耦合的人机-任务交互。学习到的通信动作使机器人能够决定何时以及向哪个操作员查询，以及何时与同伴共享信息。分配和通信通过协作式多智能体强化学习联合优化，使团队能够在管理有限通信和监督资源的同时获取有用信息。我们还引入了一个基准测试，整合了异构人类和机器人、动态任务和操作状态，以及受限的机器人间和双向人机链路。在多达16个机器人、6个人类和112个任务的异构团队上的实验表明，随着协调规模的增加，CommHG提高了及时加权的任务完成率，在大型场景中超越了最强基线。
英文摘要
Multi-human multi-robot (MH-MR) teams combine robotic autonomy with human expertise, but effective task allocation requires coordinating scarce, dynamically available human support with distributed robot execution. Limited robot-robot and human-robot communication further complicates this coupling by delaying information exchange and supervisory intervention. We introduce CommHG, a communication-aware heterogeneous graph learning framework for decentralized MH-MR task allocation. CommHG represents coupled human-robot-task interactions through local graphs conditioned on information availability and age. Learned communication actions enable robots to decide when and which operator to query, and when to share information with peers. Allocation and communication are jointly optimized through cooperative multi-agent reinforcement learning, allowing the team to acquire useful information while managing limited communication and supervisory resources. We also introduce a benchmark integrating heterogeneous humans and robots, dynamic tasks and operational states, and constrained robot-robot and bidirectional human-robot links. Experiments across heterogeneous teams with up to 16 robots, 6 humans, and 112 tasks show that CommHG improves timely weighted mission completion as coordination scale increases, outperforming the strongest baseline in the Large scenario.
