按逻辑行走：面向双足运动抗外部扰动的信号时序逻辑引导模型预测控制
Walking-by-Logic: Signal Temporal Logic-Guided Model Predictive Control for Bipedal Locomotion Resilient to External Perturbations
AI总结：
本文首次将信号时序逻辑引导的模型预测控制用于双足运动推挤恢复，生成同时满足任务规范并最大化鲁棒性的安全轨迹，在交叉腿动作和踏脚石选择等任务中优于现有控制器。
摘要：
本研究提出了一种基于模型预测控制公式的新型规划框架，该框架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运动推挤恢复的研究，在该场景中机器人会遭遇意外扰动。现有的恢复策略往往难以处理复杂的任务逻辑推理和运动鲁棒性评估，导致其容易因恢复策略不当或鲁棒性不足而失败。为解决这一问题，STL引导框架生成最优且安全的恢复轨迹，同时满足任务规范并最大化运动鲁棒性。我们的框架在高保真动态仿真中优于最先进的运动控制器，尤其是在涉及交叉腿动作的场景中。此外，它还在诸如踏脚石上的运动等任务中展示了多功能性，在这些任务中机器人必须从一组不连续的落脚点中进行选择才能成功机动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
