Advising Agent for Supporting Human-Multi-Drone Team Collaboration

2025-02-25

AI Summary

In high-dynamics, high-uncertainty search-and-rescue missions, excessive cognitive load on human operators degrades multi-UAV coordination performance. To address this, we propose a lightweight, interpretable intelligent advisory agent. The agent integrates few-shot imitation learning with human-like trajectory generation, enabling diverse, human-preference-aligned decision trajectories from minimal human demonstrations. Coupled with generalized modeling and a real-time inference framework, it efficiently predicts long-horizon advisory outcomes and dynamically optimizes recommendations. Human-subject experiments demonstrate significant improvements: +27% task completion rate, −34% response latency, and an 89% recommendation acceptance rate. Crucially, the system achieves this while preserving interpretability, real-time responsiveness, and cross-task generalization capability—bridging critical gaps between human-centered autonomy and operational efficacy in complex, time-critical scenarios.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: Human-Robot Interaction
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Enhancing human-multi-drone team collaboration
• Assisting real-time decision-making in SAR missions
• Generating human-like trajectories for advice accuracy

Innovation

• Advising agent enhances team collaboration
• Machine learning predicts advice effects
• Human-like trajectories generated from demonstrations