Advising Agent for Supporting Human-Multi-Drone Team Collaboration

TL;DR

This paper presents an advising agent that enhances human-multi-drone team collaboration, especially in search and rescue scenarios, by predicting the effects of advice using machine learning trained on human demonstrations.

Contribution

The work introduces a novel advising agent that uses human demonstrations and machine learning to provide effective real-time support in complex drone-human teams.

Findings

Improved team performance with the advising agent

High-quality assistance demonstrated through human evaluations

Effective prediction of long-term advice effects

Abstract

Multi-drone systems have become transformative technologies across various industries, offering innovative applications. However, despite significant advancements, their autonomous capabilities remain inherently limited. As a result, human operators are often essential for supervising and controlling these systems, creating what is referred to as a human-multi-drone team. In realistic settings, human operators must make real-time decisions while addressing a variety of signals, such as drone statuses and sensor readings, and adapting to dynamic conditions and uncertainty. This complexity may lead to suboptimal operations, potentially compromising the overall effectiveness of the team. In critical contexts like Search And Rescue (SAR) missions, such inefficiencies can have costly consequences. This work introduces an advising agent designed to enhance collaboration in human-multi-drone teams, with a specific focus on SAR scenarios. The advising agent is designed to assist the human operator by suggesting contextual actions worth taking. To that end, the agent employs a novel computation technique that relies on a small set of human demonstrations to generate varying realistic human-like trajectories. These trajectories are then generalized using machine learning for fast and accurate predictions of the long-term effects of different advice. Through human evaluations, we demonstrate that our approach delivers high-quality assistance, resulting in significantly improved performance compared to baseline conditions.