Stable bipedal walking is a key prerequisite for humanoid robots to reach their potential of being versatile helpers in our everyday environments. Bipedal walking is, however, a complex motion that requires the coordination of many degrees of freedom while it is also inherently unstable and sensitive to disturbances. The balance of a walking biped has to be constantly maintained. The most effective ways of controlling balance are well timed and placed recovery steps — capture steps — that absorb the expense momentum gained from a push or a stumble. We present a bipedal gait generation framework that utilizes step timing and foot placement techniques in order to recover the balance of a biped even after strong disturbances. Our framework modifies the next footstep location instantly when responding to a disturbance and generates controllable omnidirectional walking using only very little sensing and computational power. We exploit the open-loop stability of a central pattern generated gait to fit a linear inverted pendulum model (LIPM) to the observed center of mass (CoM) trajectory. Then, we use the fitted model to predict suitable footstep locations and timings in order to maintain balance while following a target walking velocity. Our experiments show qualitative and statistical evidence of one of the strongest push-recovery capabilities among humanoid robots to date.

中文翻译：

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捕获步骤：人形机器人的稳健行走

稳定的双足行走是人形机器人在我们的日常环境中发挥其作为多功能助手的潜力的关键先决条件。然而，双足行走是一种复杂的运动，需要许多自由度的协调，同时它本身也不稳定且对干扰很敏感。步行 Biped 的平衡必须不断保持。控制平衡的最有效方法是适时并安排好的恢复步骤——捕捉步骤——吸收从推动或绊倒中获得的消耗动力。我们提出了一种双足步态生成框架，该框架利用步态时间和足部放置技术，以便即使在强烈干扰后也能恢复两足动物的平衡。我们的框架在响应干扰时立即修改下一个脚步位置，并仅使用非常少的传感和计算能力生成可控的全向行走。我们利用中心模式生成的步态的开环稳定性将线性倒立摆模型 (LIPM) 拟合到观察到的质心 (CoM) 轨迹。然后，我们使用拟合模型来预测合适的脚步位置和时间，以便在遵循目标步行速度的同时保持平衡。我们的实验显示了迄今为止人形机器人中最强的推动恢复能力之一的定性和统计证据。我们利用中心模式生成的步态的开环稳定性将线性倒立摆模型 (LIPM) 拟合到观察到的质心 (CoM) 轨迹。然后，我们使用拟合模型来预测合适的脚步位置和时间，以便在遵循目标步行速度的同时保持平衡。我们的实验显示了迄今为止人形机器人中最强的推动恢复能力之一的定性和统计证据。我们利用中心模式生成的步态的开环稳定性将线性倒立摆模型 (LIPM) 拟合到观察到的质心 (CoM) 轨迹。然后，我们使用拟合模型来预测合适的脚步位置和时间，以便在遵循目标步行速度的同时保持平衡。我们的实验显示了迄今为止人形机器人中最强的推动恢复能力之一的定性和统计证据。