Capability Augmentation for Heterogeneous Dynamic Teaming with Temporal Logic Tasks

TL;DR

This paper introduces a framework combining Capability-Augmenting Tasks with Metric Temporal Logic to enable heterogeneous multi-agent teams to improve performance through capability sharing, using a centralized optimization approach.

Contribution

It presents a novel integration of capability augmentation into temporal logic specifications and trajectory synthesis for multi-agent systems.

Findings

Enhanced agent performance through capability sharing.

Simpler task specifications compared to baseline.

Improved expressivity over CaTL+.

Abstract

This paper considers how heterogeneous multi-agent teams can leverage their different capabilities to mutually improve individual agent performance. We present Capability-Augmenting Tasks (CATs), which encode how agents can augment their capabilities based on interactions with other teammates. Our framework integrates CAT into the semantics of Metric Temporal Logic (MTL), which defines individual spatio-temporal tasks for all agents. A centralized Mixed-Integer Program (MIP) is used to synthesize trajectories for all agents. We compare the expressivity of our approach to a baseline of Capability Temporal Logic Plus (CaTL+). Case studies demonstrate that our approach allows for simpler specifications and improves individual performance when agents leverage the capabilities of their teammates.