LLM駆動マルチロボットシステムにおける操作された主張による行動連鎖の封じ込め
Containing Behavioral Cascades from Manipulated Claims in LLM-Powered Multi-Robot Systems
LLMで動くマルチロボットシステムが偽の世界状態主張にだまされた際、検証・適応・保留の計画で被害の連鎖をチーム全体に広げず封じ込める枠組みを提案した。
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?
3. 技術・手法の肝は?
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著者: Waleed Bin Khalid, Byung-Cheol Min
分類: cs.RO
原文アブストラクト
Large language model (LLM)-powered multi-robot systems are vulnerable to semantic manipulation: an accepted false world-state claim can trigger a fleet-wide behavioral cascade, causing unnecessary replanning, increased path costs, congestion, or apparent mission infeasibility. Conditioning on a successful manipulation, we propose an active verification framework that contains its downstream effects before they propagate across the fleet. A dedicated verification module generates a structured Verify-Adapt-Hold plan: selected robots inspect consequential regions, a limited subset provisionally adapts when necessary, and the remaining robots retain their trusted plans. We evaluate the framework in a multi-robot transportation environment using injected false obstacle claims across different impacts and team sizes. Evaluation measures cascade containment, Sum-of-Costs, makespan, and coverage ratio. Results show that treating post-compromise verification as a team-level planning problem, rather than a binary trust decision, effectively limits the cascading physical consequences of semantic manipulation.