Logic-VLA: 時間論理条件付き視覚言語行動モデル
Logic-VLA: A Temporal Logic Conditioned Vision-Language-Action Model
自然言語指示に加えて、推論時に与えられる信号時相論理（STL）仕様を満たすように行動を調整するVLAモデルを提案。STLエンコーダと2段階のポリシー適応により、形式要件の充足率を大幅に向上させつつ、通常のタスク成功率をほぼ維持する。
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？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Celina Shiyu Wang, Yiqi Zhao, Junjie Ye, Yue Wang, Jyotirmoy V. Deshmukh
分類: cs.RO, cs.LO, eess.SY
原文アブストラクト
Vision-language-action (VLA) models can follow natural-language (NL) task instructions, but such instructions may not precisely specify safety-critical or spatiotemporal requirements on the resulting behavior. We introduce Logic-VLA, a formal-requirement-aware VLA that conditions on Signal Temporal Logic (STL) specifications supplied at inference time. Logic-VLA uses a syntax-graph-based STL encoder pre-trained to capture temporal logic semantics. Policy adaptation proceeds in two stages: STL-conditioned supervised fine-tuning on satisfying demonstrations is followed by trajectory-level preference optimization over matched satisfying-violating rollout pairs using a flow-matching surrogate for Identity Preference Optimization. This formulation improves formal requirement satisfaction while preserving the nominal NL task. We evaluate Logic-VLA in closed-loop quadcopter navigation simulation across randomized photorealistic environments and test generalization to STL formulas unseen during training. Across the evaluation benchmarks, Logic-VLA improves STL satisfaction rate over an STL-blind base policy by 24.8 to 40.7 percentage points (pp) while reducing nominal NL task success by at most 1.8 pp, showing that a single VLA can adapt its behavior to varying formal requirements without requiring a separate policy for each specification.