Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation

• URL: http://arxiv.org/abs/2603.07824v1
• Date: Sun, 08 Mar 2026 22:00:32 GMT
• Title: Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation
• Abstract summary: Human-AI joint planning in Unmanned Aerial Vehicles (UAVs) typically relies on control handover when facing environmental uncertainties.<n>We propose a novel framework that shifts the collaboration paradigm from control takeover to active information elicitation.
• Score: 15.477731207334793
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-AI joint planning in Unmanned Aerial Vehicles (UAVs) typically relies on control handover when facing environmental uncertainties, which is often inefficient and cognitively demanding for non-expert operators. To address this, we propose a novel framework that shifts the collaboration paradigm from control takeover to active information elicitation. We introduce the Minimal Information Neuro-Symbolic Tree (MINT), a reasoning mechanism that explicitly structures knowledge gaps regarding obstacles and goals into a queryable format. By leveraging large language models, our system formulates optimal binary queries to resolve specific ambiguities with minimal human interaction. We demonstrate the efficacy of this approach through a comprehensive workflow integrating a vision-language model for perception, voice interfaces, and a low-level UAV control module in both high-fidelity NVIDIA Isaac simulations and real-world deployments. Experimental results show that our method achieves a significant improvement in the success rate for complex search-and-rescue tasks while significantly reducing the frequency of human interaction compared to exhaustive querying baselines.

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