发表机构
Tampere University（坦佩雷大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对STL鲁棒性评估的计算瓶颈，提出ScanSTL并行算法，实现线性复杂度与对数深度，在CPU和GPU上分别获得243倍和104倍加速，并提升机器人MPC修复效率。
AI 中文摘要
在机器人规划与控制中，信号时序逻辑（STL）鲁棒性的重复评估和微分可能成为计算瓶颈。顺序的时间递归限制了并行性，而密集掩码增加了内存需求。我们提出ScanSTL，它将结合了关联时间聚合与并行扫描及有序块归约。Eventually和Always使用范围极值，而包含性强Until则组合紧凑的段表示。一个通用的范围引擎处理有界和移位区间，包括延迟Until见证之前所需的守卫。每个精确的时间算子在线性工作和存储以及均匀采样有限信号上的对数并行深度下计算完整的鲁棒性轨迹。一个开源的JAX实现支持自动微分、批处理和编译。我们使用CPU和GPU算子基准测试以及九个组合规范，将ScanSTL与STLCG和STLCG++进行比较。在这九个规范中，在512个样本下，ScanSTL在CPU上的JAX中，相对于STLCG++，前向评估的几何平均加速比为243倍，梯度计算的几何平均加速比为104倍。在RTX 5090 GPU上，ScanSTL评估超过两百万个样本的无界Until，前向评估的中位时间低于0.1毫秒，梯度计算的中位时间低于0.25毫秒。使用机器狗进行的模拟护航和巡逻实验进一步证明了在模型预测控制中，违规计划的修复速度更快，求解器容量更大。
英文摘要
Repeated evaluation and differentiation of Signal Temporal Logic (STL) robustness can become a computational bottleneck in robot planning and control. Sequential temporal recurrences limit parallelism, while dense masking increases memory requirements. We propose ScanSTL, which combines associative temporal aggregation with parallel scans and ordered block reductions. Eventually and Always use range extrema, while inclusive strong Until composes compact segment representations. A common range engine handles bounded and shifted intervals, including the guards required before delayed Until witnesses. Each exact temporal operator computes complete robustness traces with linear work and storage and logarithmic parallel depth on uniformly sampled finite signals. An open source JAX implementation supports automatic differentiation, batching, and compilation. We compare ScanSTL with STLCG and STLCG++ using CPU and GPU operator benchmarks and nine composed specifications. Across these nine specifications at 512 samples, ScanSTL achieves geometric mean speedups of 243 times for forward evaluation and 104 times for gradient computation over STLCG++ in JAX on the CPU. On an RTX~5090 GPU, ScanSTL evaluates unbounded Until over more than two million samples with median times below 0.1 ms for forward evaluation and 0.25 ms for gradient computation. Simulated escort and patrol experiments with a robot dog further demonstrate faster repair of violating plans and greater solver capacity in model predictive control.
Comments8 pages, 5 figures
