This study proposes a novel planning framework based on a model predictive control formulation that incorporates signal temporal logic (STL) specifications for task completion guarantees and robustness quantification. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion push recovery, where the robot experiences unexpected disturbances. Existing recovery strategies often struggle with complex task logic reasoning and locomotion robustness evaluation, making them susceptible to failures caused by inappropriate recovery strategies or insufficient robustness. To address this issue, the STL-guided framework generates optimal and safe recovery trajectories that simultaneously satisfy the task specification and maximize the locomotion robustness. Our framework outperforms a state-of-the-art locomotion controller in a high-fidelity dynamic simulation, especially in scenarios involving crossed-leg maneuvers. Furthermore, it demonstrates versatility in tasks such as locomotion on stepping stones, where the robot must select from a set of disjointed footholds to maneuver successfully.

翻译：本研究提出了一种基于模型预测控制框架的新型规划方法，该方法融合了信号时序逻辑（STL）规范，以实现任务完成保证和鲁棒性量化。这是首次将STL引导的轨迹优化应用于双足行走推力恢复中（机器人受到意外扰动时）的研究。现有恢复策略通常难以处理复杂的任务逻辑推理和行走鲁棒性评估，容易因不当的恢复策略或鲁棒性不足而导致失败。为解决这一问题，本文提出的STL引导框架能够生成既满足任务规范又最大化行走鲁棒性的最优安全恢复轨迹。在高保真动态仿真中，该框架在跨腿机动等场景下显著优于现有最先进的行走控制器。此外，该方法在跳石行走等任务中展示了多功能性——机器人需从一组分离的落脚点中选择合适位置以成功完成机动。