LLM駆動の自己洗練による実体ドローンタスクプランニング
LLM-Driven Self-Refinement for Embodied Drone Task Planning
産業用ドローンのタスク計画において、連続状態評価と階層的ビヘイビアツリー修正によりLLMが経験から自己洗練するシステムSRDroneを提案し、成功率を大幅に向上させた。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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4. どうやって有効だと検証した？
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著者: Deyu Zhang, Xicheng Zhang, Jiahao Li, Tingting Long, Xunhua Dai, Yongjian Fu, Jinrui Zhang, Ju Ren, Yaoxue Zhang
分類: cs.RO, cs.AI
原文アブストラクト
We introduce SRDrone, a novel system designed for self-refinement task planning in industrial-grade embodied drones. SRDrone incorporates two key technical contributions: First, it employs a continuous state evaluation methodology to robustly and accurately determine task outcomes and provide explanatory feedback. This approach supersedes conventional reliance on single-frame final-state assessment for continuous, dynamic drone operations. Second, SRDrone implements a hierarchical Behavior Tree (BT) modification model. This model integrates multi-level BT plan analysis with a constrained strategy space to enable structured reflective learning from experience. Experimental results demonstrate that SRDrone achieves a 44.87% improvement in Success Rate (SR) over baseline methods. Furthermore, real-world deployment utilizing an experience base optimized through iterative self-refinement attains a 96.25% SR. By embedding adaptive task refinement capabilities within an industrial-grade BT planning framework, SRDrone effectively integrates the general reasoning intelligence of Large Language Models (LLMs) with the stringent physical execution constraints inherent to embodied drones. Code is available at https://github.com/ZXiiiC/SRDrone.
