基于逻辑的鲁棒运动：通过信号时序逻辑引导的模型预测控制实现抗扰动双足运动
Robust-Locomotion-by-Logic: Perturbation-Resilient Bipedal Locomotion via Signal Temporal Logic Guided Model Predictive Control
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，通过解析稳定性度量和数据驱动碰撞约束实现抗平移与旋转扰动的鲁棒恢复，并在Cassie机器人上完成仿真与硬件验证。
摘要：
本研究引入了一个鲁棒规划框架，该框架利用模型预测控制（MPC）方法，并通过融入信号时序逻辑（STL）规范加以增强。这标志着首次将STL引导的轨迹优化应用于双足运动的研究，专门设计用于同时处理平移扰动和旋转扰动。现有的恢复策略往往难以对复杂任务逻辑进行推理并系统性地评估运动鲁棒性，使其容易因恢复策略不当或鲁棒性不足而失败。为解决这些问题，我们为双足运动设计了一个解析稳定性度量，并使用STL规范对该度量进行量化，从而引导恢复轨迹的生成以实现最大鲁棒度。为了实现安全且计算高效的交叉腿动作，我们设计了数据驱动的自腿碰撞约束，其速度比传统的基于逆运动学的方法快1000倍。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、不含STL的标准MPC以及基于线性时序逻辑的规划器，尤其是在涉及交叉腿动作的场景中。此外，Cassie双足机器人在水平扰动和旋转扰动（例如船舶运动中观察到的扰动）下实现了鲁棒性能。这些环境在仿真中得到了验证，并部署到了硬件上。此外，我们提出的方法在踏脚石上展示了通用性，并在倾斜地形上展示了与地形无关的特性。
英文摘要：
This study introduces a robust planning framework that utilizes a model predictive control (MPC) approach, enhanced by incorporating signal temporal logic (STL) specifications. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion, specifically designed to handle both translational and orientational perturbations. Existing recovery strategies often struggle with reasoning complex task logic and evaluating locomotion robustness systematically, making them susceptible to failures caused by inappropriate recovery strategies or lack of robustness. To address these issues, we design an analytical stability metric for bipedal locomotion and quantify this metric using STL specifications, which guide the generation of recovery trajectories to achieve maximum robustness degree. To enable safe and computational-efficient crossed-leg maneuver, we design data-driven self-leg-collision constraints that are $1000$ times faster than the traditional inverse-kinematics-based approach. Our framework outperforms a state-of-the-art locomotion controller, a standard MPC without STL, and a linear-temporal-logic-based planner in a high-fidelity dynamic simulation, especially in scenarios involving crossed-leg maneuvers. Additionally, the Cassie bipedal robot achieves robust performance under horizontal and orientational perturbations such as those observed in ship motions. These environments are validated in simulations and deployed on hardware. Furthermore, our proposed method demonstrates versatility on stepping stones and terrain-agnostic features on inclined terrains.
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。
