Drawing inspiration from human multi-domain walking, this work presents a novel reduced-order model based framework for realizing multi-domain robotic walking. At the core of our approach is the viewpoint that human walking can be represented by a hybrid dynamical system, with continuous phases that are fully-actuated, under-actuated, and over-actuated and discrete changes in actuation type occurring with changes in contact. Leveraging this perspective, we synthesize a multi-domain linear inverted pendulum (MLIP) model of locomotion. Utilizing the step-to-step dynamics of the MLIP model, we successfully demonstrate multi-domain walking behaviors on the bipedal robot Cassie -- a high degree of freedom 3D bipedal robot. Thus, we show the ability to bridge the gap between multi-domain reduced order models and full-order multi-contact locomotion. Additionally, our results showcase the ability of the proposed method to achieve versatile speed-tracking performance and robust push recovery behaviors.

翻译：受人类多域行走的启发，本文提出一种基于降阶模型的框架，用于实现机器人多域行走。该方法的核心思想在于将人类行走视为一个混合动力学系统，包含全驱动、欠驱动和过驱动连续阶段，且驱动类型的离散变化随接触状态的改变而发生。基于这一视角，我们构建了多域线性倒立摆（MLIP）运动模型。通过利用MLIP模型的步态间动力学特性，我们成功在双足机器人Cassie——一台高自由度的三维双足机器人上——演示了多域行走行为。由此，我们展示了弥合多域降阶模型与全阶多接触运动之间差距的能力。此外，实验结果表明，所提方法能够实现优异的变速跟踪性能与鲁棒推扰恢复行为。