多机器人在不确定环境下，自适应分配任务并保证时序逻辑约束达标。
Adaptive Bi-Level Multi-Robot Task Allocation and Learning under Uncertainty with Temporal Logic Constraints
- 分层框架：高层分配任务，底层分布式学习优化
- 基于实时数据迭代更新任务成功率和奖励，无需已知机器人力学模型
- 理论证明可满足用户设定的概率阈值，适合复杂动态任务
本文解决未知机器人转移模型下的多机器人协调问题，确保由时窗时序逻辑指定的任务在用户定义的概率阈值下被满足。提出一种双层框架：(i) 高层任务分配，根据机器人的任务完成概率估计和预期奖励进行分配；(ii) 低层分布式策略学习与执行，机器人在完成指派任务的同时独立优化辅助奖励。为应对机器人动态的不确定性，该方法利用实时任务执行数据，迭代更新任务完成概率与奖励估计，实现无需显式机器人转移模型的自适应任务分配。我们从理论上验证了所提算法，证明任务分配在高置信度下满足期望概率阈值。最后，通过全面仿真验证了该框架的有效性。
原文摘要 · Abstract (English)
This work addresses the problem of multi-robot coordination under unknown robot transition models, ensuring that tasks specified by Time Window Temporal Logic are satisfied with user-defined probability thresholds. We present a bi-level framework that integrates (i) high-level task allocation, where tasks are assigned based on the robots' estimated task completion probabilities and expected rewards, and (ii) low-level distributed policy learning and execution, where robots independently optimize auxiliary rewards while fulfilling their assigned tasks. To handle uncertainty in robot dynamics, our approach leverages real-time task execution data to iteratively refine expected task completion probabilities and rewards, enabling adaptive task allocation without explicit robot transition models. We theoretically validate the proposed algorithm, demonstrating that the task assignments meet the desired probability thresholds with high confidence. Finally, we demonstrate the effectiveness of our framework through comprehensive simulations.
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