Logic-VLA：一种时序逻辑条件下的视觉-语言-动作模型

中文摘要 AI 辅助

视觉-语言-动作（VLA）模型可遵循自然语言（NL）任务指令，但这类指令可能无法精确规定最终行为对安全关键或时空特性的要求。我们提出Logic-VLA，一种感知形式化需求的VLA，它在推理时以信号时序逻辑（STL）规约为条件。Logic-VLA使用基于语法图的STL编码器，该编码器经预训练以捕捉时序逻辑语义。策略适配分两个阶段进行：首先在满足要求的演示上进行STL条件下的有监督微调，随后使用身份偏好优化的流匹配代理，在匹配的满足-违反轨迹对之上进行轨迹级偏好优化。该方法在保持常规自然语言任务性能的同时，提升了形式化需求的满足度。我们在闭环四旋翼无人机导航仿真中，在随机生成的逼真环境中评估Logic-VLA，并测试其对训练时未见过的STL公式的泛化能力。在评估基准中，Logic-VLA将STL满足率较无STL的基础策略提升了24.8至40.7个百分点（pp），同时常规自然语言任务成功率最多仅降低1.8个百分点，表明单个VLA可针对不同形式化需求调整自身行为，无需为每个规约单独训练策略。

英文摘要

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.

发表机构

• University of Southern California（南加州大学）

机构由 AI 辅助整理，请以论文原文为准。