Hybrid Stepping Motion Generation for Wheeled-Bipedal Robots Without Roll Joints on Legs

Abstract

Wheeled-bipedal robots without roll joints on legs, such as Handle by Boston Dynamics and Ascento by ETH, have drawn increasing attention due to their superior motion agility but pose unique challenges to motion generation. So far, there is little to no research on how to enable these robots to step forward with their legs. In this study, we will explore hybrid stepping locomotion strategies via a two-phase design procedure. During the single-leg support phase, a two-mass variable height inverted pendulum model will be used for stepping locomotion generation and control. As for the double-leg support phase, given the difficulty of modeling contact sliding, friction, and collision, a model-free reinforcement learning approach is employed to leverage the rich data for reliable motion generation. Experiments on our own developed wheeled-bipedal robot Ollie demonstrate that the robot is capable of stepping forward with varied stepping frequencies. Stepping with yaw rotation and tests in different scenarios show the efficacy and robustness of the hybrid stepping motion generation method.