Signal Temporal Logic (STL) is a powerful specification language for describing complex temporal behaviors of continuous signals, making it well-suited for high-level robotic task descriptions. However, generating executable plans for STL tasks is challenging, as it requires consideration of the coupling between the task specification and the system dynamics. Existing approaches either follow a model-based setting that explicitly requires knowledge of the system dynamics or adopt a task-oriented data-driven approach to learn plans for specific tasks. In this work, we address the problem of generating executable STL plans for systems with unknown dynamics. We propose a hierarchical planning framework that enables zero-shot generalization to new STL tasks by leveraging only task-agnostic trajectory data during offline training. The framework consists of three key components: (i) decomposing the STL specification into several progresses and time constraints, (ii) searching for timed waypoints that satisfy all progresses under time constraints, and (iii) generating trajectory segments using a pre-trained diffusion model and stitching them into complete trajectories. We formally prove that our method guarantees STL satisfaction, and simulation results demonstrate its effectiveness in generating dynamically feasible trajectories across diverse long-horizon STL tasks.

翻译：信号时序逻辑（STL）是一种用于描述连续信号复杂时序行为的强大规约语言，非常适合高层机器人任务描述。然而，为STL任务生成可执行规划具有挑战性，因为它需要考虑任务规约与系统动力学之间的耦合关系。现有方法要么遵循基于模型的设定，明确要求已知系统动力学；要么采用面向任务的数据驱动方法，为特定任务学习规划。在本工作中，我们针对动力学未知的系统，研究生成可执行STL规划的问题。我们提出一种分层规划框架，通过在离线训练中仅利用任务无关的轨迹数据，实现对新STL任务的零样本泛化。该框架包含三个关键组成部分：（i）将STL规约分解为多个进度项与时间约束；（ii）在时间约束下搜索满足所有进度项的时序路径点；（iii）使用预训练的扩散模型生成轨迹段，并将其拼接为完整轨迹。我们严格证明了该方法能保证STL规约的满足性，仿真结果验证了其在多样化长时域STL任务中生成动力学可行轨迹的有效性。