Humanoid Walking Stabilization via Model Predictive Control with Step Adjustment Based on the 3D Divergent Component of Motion
Gyeongjae Park, Myeong-Ju Kim, Kwanwoo Lee, Jaeheung Park
Abstract
In this paper, as an approach to stabilize humanoid walking where the height of CoM varies, a Novel Model Predictive Control framework based on three dimensional Divergent Component of Motion (3D-DCM) is proposed. To ensure the feasible utilization of contact forces for maintaining humanoid balance, constraints on the control inputs, Virtual Repellent Point (VRP) and footstep adjustment, and their correlation are analytically formulated into a quadratic form, resulting a Quadratically Constrained Quadratic Programming. Additionally, to enable the humanoid robot to withstand disturbances over a broader range of strides or safely navigates various terrains without encountering knee stretch, the distance between the CoM and the foot is constrained in the 3D-CoM trajectory planner. The effectiveness of the proposed method is validated through simulations and real-robot experiments in scenarios involving external disturbances and step down.
BibTeX
@inproceedings{icra2025_humanoidwalkings,
title = {Humanoid Walking Stabilization via Model Predictive Control with Step Adjustment Based on the 3D Divergent Component of Motion},
author = {Gyeongjae Park and Myeong-Ju Kim and Kwanwoo Lee and Jaeheung Park},
booktitle = {ICRA 2025},
year = {2025}
}