A Model Predictive Capture Point Control Framework for Robust Humanoid Balancing via Ankle, Hip, and Stepping Strategies

TL;DR

This paper introduces a Model Predictive Control framework for humanoid balance that integrates ankle, hip, and stepping strategies, improving robustness and performance in simulations and real robot experiments.

Contribution

It presents a novel MPC-based balance control framework with variable weighting and hierarchical structure, unifying multiple strategies for enhanced humanoid stability.

Findings

Outperforms state-of-the-art QP-based CP controller in robustness

Validated through simulations and real robot experiments

Achieves superior balancing performance with integrated strategies

Abstract

The robust balancing capability of humanoids is essential for mobility in real environments. Many studies focus on implementing human-inspired ankle, hip, and stepping strategies to achieve human-level balance. In this paper, a robust balance control framework for humanoids is proposed. Firstly, a Model Predictive Control (MPC) framework is proposed for Capture Point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. Additionally, a variable weighting method is introduced that adjusts the weighting parameters of the Centroidal Angular Momentum damping control. Secondly, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated compared to a state-of-the-art Quadratic Programming-based CP controller that employs the ankle, hip, and stepping strategies.