Signal Temporal Logic (STL) is a powerful language for specifying temporally structured robotic tasks. Planning executable trajectories under STL constraints remains difficult when system dynamics and environment structure are not analytically available. Existing methods typically either assume explicit models or learn task-specific behaviors, limiting zero-shot generalization to unseen STL tasks. In this work, we study offline STL planning under unknown dynamics using only task-agnostic trajectory data. Our central design philosophy is to separate logical reasoning from trajectory realization. We instantiate this idea in DAG-STL, a hierarchical framework that converts long-horizon STL planning into three stages. It first decomposes an STL formula into reachability and invariance progress conditions linked by shared timing constraints. It then allocates timed waypoints using learned reachability-time estimates. Finally, it synthesizes trajectories between these waypoints with a diffusion-based generator. This decomposition--allocation--generation pipeline reduces global planning to shorter, better-supported subproblems. To bridge the gap between planning-level correctness and execution-level feasibility, we further introduce a rollout-free dynamic consistency metric, an anytime refinement search procedure for improving multiple allocation hypotheses under finite budgets, and a hierarchical online replanning mechanism for execution-time recovery. Experiments in Maze2D, OGBench AntMaze, and the Cube domain show that DAG-STL substantially outperforms direct robustness-guided diffusion on complex long-horizon STL tasks and generalizes across navigation and manipulation settings. In a custom environment with an optimization-based reference, DAG-STL recovers most model-solvable tasks while retaining a clear computational advantage over direct optimization based on the explicit system model.

翻译：信号时序逻辑（STL）是一种用于描述具有时间结构的机器人任务的强大语言。在系统动力学与环境结构无法解析获取的情况下，规划满足STL约束的可执行轨迹仍具有挑战性。现有方法通常假设显式模型或学习特定任务行为，限制了其对未见STL任务的零样本泛化能力。本文研究仅利用任务无关轨迹数据在未知动力学条件下实现离线STL规划。我们的核心设计理念是将逻辑推理与轨迹实现相分离。该思想在分层框架DAG-STL中得到实例化，该框架将长时域STL规划转化为三个阶段：首先将STL公式分解为由共享时序约束链接的可达性与不变性进度条件，其次利用学习的可达性时间估计分配带时间约束的路径点，最后通过基于扩散的生成器合成路径点间的轨迹。这种"分解-分配-生成"流水线将全局规划简化为更短且支持度更高的子问题。为弥合规划层面正确性与执行层面可行性之间的差距，我们进一步引入免rollout的动态一致性指标、在有限预算下优化多分配假设的任意时间精炼搜索过程，以及用于执行时间恢复的分层在线重规划机制。在Maze2D、OGBench AntMaze及Cube领域上的实验表明，DAG-STL在复杂长时域STL任务中显著优于直接鲁棒性引导的扩散方法，并能泛化至导航与操作场景。在基于优化基准的自定义环境中，DAG-STL恢复大部分模型可解任务，同时相较基于显式系统模型的直接优化保持明显计算优势。