This paper presents a framework for synthesizing bipedal robotic walking that adapts to unknown environment and dynamics error via a data-driven step-to-step (S2S) dynamics model. We begin by synthesizing an S2S controller that stabilizes the walking using foot placement through nominal S2S dynamics from the hybrid linear inverted pendulum (H-LIP) model. Next, a data-driven representation of the S2S dynamics of the robot is learned online via classical adaptive control methods. The desired discrete foot placement on the robot is thereby realized by proper continuous output synthesis capturing the data-driven S2S controller coupled with a low-level tracking controller. The proposed approach is implemented in simulation on an underactuated 3D bipedal robot, Cassie, and improved reference velocity tracking is demonstrated. The proposed approach is also able to realize walking behavior that is robustly adaptive to unknown loads, inaccurate robot models, external disturbance forces, biased velocity estimation, and unknown slopes.

翻译：本文展示了一个通过数据驱动的一步至一步(S2S)动态模型,将双环机器人行走综合起来以适应未知环境和动态错误的框架。 我们首先将一个S2S控制器综合起来,该控制器通过标称 S2S 动态,通过混合线性倒转曲盘(H-LIP)模型,用脚定位来稳定行走。接下来,通过经典的适应性控制方法,在线学习机器人S2S动态的数据驱动表示。因此,在机器人上所需的离散脚位置通过适当的连续产出合成来捕捉数据驱动的S2S控制器和一个低级跟踪控制器来实现。 提议的方法是在模拟一个未完全激活的 3D双向机器人、 Cassi、 和改进的参考速度跟踪过程中实施的。 拟议的方法还能够实现行走行为,该行为对未知的负载、不精确的机器人模型、外部扰动力、偏差速度估计和未知的斜坡度具有很强的适应性。