Robust Disturbance Rejection for Robotic Bipedal Walking: System-Level-Synthesis with Step-to-step Dynamics Approximation

TL;DR

This paper introduces a robust control method for bipedal robots that uses learned step-to-step dynamics and system-level synthesis to effectively counter external push disturbances, ensuring stable walking.

Contribution

The paper develops a system-level synthesis approach based on learned linear step-to-step dynamics to enhance disturbance rejection in bipedal walking robots.

Findings

Successfully applied to AMBER and Cassie robots

Effectively countered external push disturbances

Demonstrated computational efficiency and generality

Abstract

We present a stepping stabilization control that addresses external push disturbances on bipedal walking robots. The stepping control is synthesized based on the step-to-step (S2S) dynamics of the robot that is controlled to have an approximately constant center of mass (COM) height. We first learn a linear S2S dynamics with bounded model discrepancy from the undisturbed walking behaviors of the robot, where the walking step size is taken as the control input to the S2S dynamics. External pushes are then considered as disturbances to the learned S2S (L-S2S) dynamics. We then apply the system-level-synthesis (SLS) approach on the disturbed L-S2S dynamics to robustly stabilize the robot to the desired walking while satisfying the kinematic constraints of the robot. We successfully realize the proposed approach on the walking of the bipedal robot AMBER and Cassie subject to push disturbances, showing that the approach is general, effective, and computationally-efficient for robust disturbance rejection.