Adaptive Bi-Level Multi-Robot Task Allocation and Learning under Uncertainty with Temporal Logic Constraints

TL;DR

This paper introduces an adaptive bi-level framework for multi-robot task allocation under uncertainty, ensuring temporal logic constraints are satisfied with high probability by integrating high-level task assignment and low-level distributed learning.

Contribution

It proposes a novel bi-level approach combining adaptive task allocation with distributed policy learning, handling unknown robot dynamics and satisfying temporal logic constraints.

Findings

Framework achieves high-confidence task satisfaction probabilities.

Adaptive allocation improves efficiency under model uncertainty.

Simulation results validate theoretical guarantees.

Abstract

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.