Control Barrier Functions (CBFs) have emerged as a powerful tool for enforcing safety in optimization-based controllers, and their integration with Signal Temporal Logic (STL) has enabled the specification-driven synthesis of complex robotic behaviors. However, existing CBF-STL approaches typically rely on fixed hyperparameters and myopic, per-time step optimization, which can lead to overly conservative behavior, infeasibility near tight input limits, and difficulty satisfying long-horizon STL tasks. To address these limitations, we propose a feasibility-aware learning framework that embeds trainable, time-varying High Order Control Barrier Functions (HOCBFs) into a differentiable Quadratic Program (dQP). Our approach provides a systematic procedure for constructing time-varying HOCBF constraints for a broad fragment of STL and introduces a unified robustness measure that jointly captures STL satisfaction, QP feasibility, and control-bound compliance. Three neural networks-InitNet, RefNet, and an extended BarrierNet-collaborate to generate reference inputs and adapt constraint-related hyperparameters automatically over time and across initial conditions, reducing conservativeness while maximizing robustness. The resulting controller achieves STL satisfaction with strictly feasible dQPs and requires no manual tuning. Simulation results demonstrate that the proposed framework maintains high STL robustness under tight input bounds and significantly outperforms fixed-parameter and non-adaptive baselines in complex environments.

翻译：控制屏障函数（CBFs）已成为基于优化的控制器中确保安全性的有力工具，其与信号时序逻辑（STL）的结合实现了复杂机器人行为的规范驱动合成。然而，现有的CBF-STL方法通常依赖固定超参数和短视的逐时间步优化，这可能导致过度保守的行为、在严格输入限制附近不可行，以及难以满足长时域STL任务。为应对这些局限，我们提出了一种可行性感知学习框架，将可训练的时变高阶控制屏障函数（HOCBFs）嵌入到可微二次规划（dQP）中。该方法为构建广泛STL片段的时变HOCBF约束提供了系统化流程，并引入了一个统一鲁棒性度量，共同捕获STL满足性、QP可行性和控制边界合规性。三个神经网络——InitNet、RefNet和扩展的BarrierNet——协同工作，随时间及跨初始条件自动生成参考输入并调整约束相关超参数，在降低保守性的同时最大化鲁棒性。所得控制器在严格可行的dQPs下实现STL满足，且无需手动调参。仿真结果表明，所提框架在严格输入限制下保持高STL鲁棒性，并在复杂环境中显著优于固定参数和非自适应基线方法。