高レベルロボット計画の安全性検証における洗練ギャップの監査
Mind the Refinement Gap: When Safe High-Level Robot Plans Produce Unsafe Executions
言語指示に基づくロボット計画と時相論理安全モニタの間で、実行時の暗黙効果が考慮されず安全判定がずれる問題を調査し、グラフベースのトレース洗練を軽量な対策として提案した。
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？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Stabak Das, Priyesh Ranjan, Xiangfang Li, Lijun Qian
分類: cs.LG, cs.RO
原文アブストラクト
Language-enabled robot systems increasingly combine semantic-graph planning with temporal-logic safety monitors. We investigate a trace-completeness assumption in these systems: whether the high-level action sequence checked by a monitor represents the navigation and implicit action effects induced during execution. We audit this assumption in RoboGuard by comparing its verdict on a surface plan with its verdict on a graph-refined trace under the same Linear Temporal Logic (LTL) specification. Our evaluation comprises 28 controlled cases spanning five action-abstraction families and 14 end-to-end cases in which SPINE [1] generates plans from natural-language instructions while RoboGuard generates scene-grounded safety specifications. In the controlled evaluation, all 12 targeted abstraction cases exhibit the predicted surface-versus-refined discrepancy while all 16 controls behave as expected, motivating graph-based trace refinement as a lightweight mitigation and a diagnostic tool for physical-AI safety monitors.