不確実性下での時相論理制約を満たす適応的二層マルチロボットタスク割当と学習
Adaptive Bi-Level Multi-Robot Task Allocation and Learning under Uncertainty with Temporal Logic Constraints
未知のロボット遷移モデル下で、時相論理で指定されたタスクを確率閾値付きで満たすため、タスク割当と分散方策学習を統合した二層フレームワークを提案し、実行データから確率を適応的に更新する。
著者: Xiaoshan Lin, Roberto Tron
分類: cs.RO, cs.AI, cs.FL
原文アブストラクト
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.
