无需训练即可在动态地图中生成符合逻辑约束的可行路径。
Zero-Shot Signal Temporal Logic Planning with Disjunctive Branch Selection in Dynamic Semantic Maps
- 用条件化Transformer加轻量启发式处理复杂或逻辑表达式。
- 在多变障碍布局下零样本泛化表现稳定，成功率显著提升。
- 适合需要快速适应新环境的安全控制任务开发者使用。
信号时序逻辑（STL）提供可验证的任务规范，在安全关键控制中至关重要。然而，STL规划仍具挑战：基于优化的方法通常过慢，而学习方法在不同环境间泛化能力差。本文提出一种适用于变地图环境的零样本STL规划求解器，可在不重新训练的情况下生成可行轨迹。通过将地图条件化的Transformer架构与轻量级启发式结合，有效处理复杂的析取（OR）子公式。此外，利用传递强化学习（TRL）确保分解后子任务在时间对齐和逻辑一致性上的统一。在具有多样化障碍布局的动态语义地图上进行实验，结果表明该框架在变化环境中展现出优越的零样本泛化能力和广泛的STL覆盖范围。
原文摘要 · Abstract (English)
Signal Temporal Logic (STL) offers verifiable task specifications and is crucial for safety-critical control. Yet STL planning remains challenging: exact optimization-based methods are often too slow, and learning-based methods struggle to generalize across varying environments. We propose a zero-shot STL planning solver for variable-map environments that generates feasible trajectories without retraining. By integrating a map-conditioned Transformer architecture with a lightweight heuristic, our approach effectively handles complex disjunctive (OR) subformulas. Furthermore, we leverage Transitive Reinforcement Learning (TRL) to ensure consistent temporal grounding and logical coherence across decomposed sub-tasks. Experiments on dynamic semantic maps with diverse obstacle layouts demonstrate consistent gains, highlighting the framework's superior zero-shot generalization to changing environments and broad STL coverage.
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