为意外做准备：针对跑步中地面不确定性的显式优化运动
Planning for the Unexpected: Explicitly Optimizing Motions for Ground Uncertainty in Running
摘要：
我们提出了一种方法来产生驱动计划的降阶，动态模型的双足动物运行。该方法明确地增强了对地面不确定性的鲁棒性。生成的计划不是积极稳定的固定身体轨迹：相反，计划与降阶模型的被动动力学相互作用，以创建紧急鲁棒性。我们的目标是为足式机器人创建计划，这些计划将对环境的不完美感知具有鲁棒性，并且能够处理过于复杂而无法实时优化的动态。工作在这个动态模型的腿运动，我们优化了一组干扰的情况下，连同标称的情况下，所有与链接的输入。由于运行模型的混合动力学，输入链接是不平凡的，但我们的解决方案是有效的，并具有分析梯度。所提出的优化过程比标准的轨迹优化要慢得多，但是产生了鲁棒的步态，该步态非常有效地拒绝干扰，而不需要任何重新规划。
英文摘要：
We propose a method to generate actuation plans for a reduced order, dynamic model of bipedal running. This method explicitly enforces robustness to ground uncertainty. The plan generated is not a fixed body trajectory that is aggressively stabilized: instead, the plan interacts with the passive dynamics of the reduced order model to create emergent robustness. The goal is to create plans for legged robots that will be robust to imperfect perception of the environment, and to work with dynamics that are too complex to optimize in real-time. Working within this dynamic model of legged locomotion, we optimize a set of disturbance cases together with the nominal case, all with linked inputs. The input linking is nontrivial due to the hybrid dynamics of the running model but our solution is effective and has analytical gradients. The optimization procedure proposed is significantly slower than a standard trajectory optimization, but results in robust gaits that reject disturbances extremely effectively without any replanning required.
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