Adapting Gait Frequency for Posture-regulating Humanoid Push-recovery via Hierarchical Model Predictive Control

TL;DR

This paper presents a hierarchical MPC approach that adapts gait frequency to improve humanoid push-recovery and posture regulation under disturbances, achieving significant improvements in recovery performance and timing.

Contribution

It introduces a novel hierarchical MPC scheme that integrates posture-aware gait adaptation for enhanced push-recovery in humanoids, addressing posture regulation often neglected in prior methods.

Findings

131% increase in maximum recoverable impulse

125 ms faster recovery stepping reflex

Reduced body attitude change under 0.2 rad

Abstract

Current humanoid push-recovery strategies often use whole-body motion, yet they tend to overlook posture regulation. For instance, in manipulation tasks, the upper body may need to stay upright and have minimal recovery displacement. This paper introduces a novel approach to enhancing humanoid push-recovery performance under unknown disturbances and regulating body posture by tailoring the recovery stepping strategy. We propose a hierarchical-MPC-based scheme that analyzes and detects instability in the prediction window and quickly recovers through adapting gait frequency. Our approach integrates a high-level nonlinear MPC, a posture-aware gait frequency adaptation planner, and a low-level convex locomotion MPC. The planners predict the center of mass (CoM) state trajectories that can be assessed for precursors of potential instability and posture deviation. In simulation, we demonstrate improved maximum recoverable impulse by 131% on average compared with baseline approaches. In hardware experiments, a 125 ms advancement in recovery stepping timing/reflex has been observed with the proposed approach. We also demonstrate improved push-recovery performance and minimized body attitude change under 0.2 rad.