This paper presents an online framework for synthesizing agile locomotion for bipedal robots that adapts to unknown environments, modeling errors, and external disturbances. To this end, we leverage step-to-step (S2S) dynamics which has proven effective in realizing dynamic walking on underactuated robots -- assuming known dynamics and environments. This paper considers the case of uncertain models and environments and presents a data-driven representation of the S2S dynamics that can be learned via an adaptive control approach that is both data-efficient and easy to implement. The learned S2S controller generates desired discrete foot placement, which is then realized on the full-order dynamics of the bipedal robot by tracking desired outputs synthesized from the given foot placement. The benefits of the proposed approach are twofold. First, it improves the ability of the robot to walk at a given desired velocity when compared to the non-adaptive baseline controller. Second, the data-driven approach enables stable and agile locomotion under the effect of various unknown disturbances: additional unmodeled payload, large robot model errors, external disturbance forces, biased velocity estimation, and sloped terrains. This is demonstrated through in-depth evaluation with a high-fidelity simulation of the bipedal robot Cassie subject to the aforementioned disturbances.

翻译：本文提出了一种在线框架，用于合成双足机器人的敏捷运动，使其能够适应未知环境、建模误差及外部干扰。为此，我们利用了步态间（Step-to-Step, S2S）动力学——该方法在假设动力学与环境已知的条件下，已被证明能有效实现欠驱动机器人的动态行走。本文考虑了模型与环境不确定的情况，提出了一种数据驱动的S2S动力学表示方法，该表示可通过一种兼具数据效率与实现简便性的自适应控制方法进行学习。学习得到的S2S控制器能够生成期望的离散足部落点，随后通过跟踪由给定足部落点合成的期望输出，在双足机器人的全阶动力学中实现该落点。所提方法具有双重优势：首先，与非自适应基线控制器相比，它提升了机器人在给定期望速度下的行走能力；其次，该数据驱动方法使机器人能在各种未知干扰（包括额外未建模负载、大幅机器人模型误差、外部干扰力、有偏速度估计及倾斜地形）影响下实现稳定且敏捷的运动。通过在双足机器人Cassie的高保真仿真中施加上述干扰并进行深入评估，验证了该方法的有效性。