发表机构
Cornell University; Imperial College London（康奈尔大学; 帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种分层多智能体强化学习框架，通过分组协调、循环预测器补偿通信丢失、高层策略生成协调参考及安全过滤器，提升仓库机器人任务完成率并保证安全。
AI 中文摘要
本文提出了一种分层多智能体强化学习框架，用于在通信丢失情况下协调仓库环境中的机器人团队。我们将机器人团队划分为多个组，组内采用集中式协调，组间采用分布式协调。每个组使用一个循环预测器来估计因通信丢失而不可用的交互信息。然后，一个更高层的策略生成一个紧凑的协调参考，该参考对组内的局部控制策略进行条件约束。一个预测性安全过滤器在提议的控制违反安全约束时对其进行评估和修改。仿真结果表明，在通信丢失情况下任务完成率提高，随着团队规模增大通信增长减少，并且在测试场景中实现了安全运行。
英文摘要
In this paper, we propose a hierarchical multi-agent reinforcement learning framework for coordinating robot teams in warehouse environments under communication loss. We partition the robot team into groups, with centralized coordination within each group and distributed coordination across groups. Each group uses a recurrent predictor to estimate unavailable interaction information due to communication loss. A higher-level policy then generates a compact coordination reference that conditions the local control policy within each group. A predictive safety filter evaluates and modifies the proposed controls when they violate safety constraints. Simulation results show improved task completion under communication loss, reduced communication growth as the team size increases, and safe operation in the tested scenarios.
Comments8 pages, 4 figures, conference
