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

Myeong-Ju Kim, Daegyu Lim, Gyeongjae Park, Kwanwoo Lee, Jaeheung Park

TL;DR

This work presents a robust humanoid balancing framework that integrates ankle, hip, and stepping strategies within a Model Predictive Control (MPC) paradigm to track Capture Point (CP) trajectories. By modeling CP–CMP dynamics with a Linear Inverted Pendulum Plus Flywheel Model (LIPFM) and introducing a variable CAM weighting scheme, the method adapts to disturbances and large perturbations. A hierarchical CP--MPC plus stepping controller determines ZMP, CAM, and footstep adjustments, while a HQP-based Whole-Body IK ensures coherent tracking of CoM, feet, and CAM. Validation includes simulations and real-robot experiments showing superior disturbance rejection and uneven-terrain robustness compared to a state-of-the-art QP-based CP controller. The approach advances real-time, multi-strategy balance control for humanoids with practical impact for reliable deployment in dynamic environments.

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.

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

TL;DR

This work presents a robust humanoid balancing framework that integrates ankle, hip, and stepping strategies within a Model Predictive Control (MPC) paradigm to track Capture Point (CP) trajectories. By modeling CP–CMP dynamics with a Linear Inverted Pendulum Plus Flywheel Model (LIPFM) and introducing a variable CAM weighting scheme, the method adapts to disturbances and large perturbations. A hierarchical CP--MPC plus stepping controller determines ZMP, CAM, and footstep adjustments, while a HQP-based Whole-Body IK ensures coherent tracking of CoM, feet, and CAM. Validation includes simulations and real-robot experiments showing superior disturbance rejection and uneven-terrain robustness compared to a state-of-the-art QP-based CP controller. The approach advances real-time, multi-strategy balance control for humanoids with practical impact for reliable deployment in dynamic environments.

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.
Paper Structure (38 sections, 23 equations, 13 figures, 3 tables)

This paper contains 38 sections, 23 equations, 13 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Fundamentals
  3. Linear Inverted Pendulum Plus Flywheel Model
  4. Capture Point dynamics based on LIPFM
  5. Overall Walking Control Structure
  6. CP--MPC: Model Predictive Control Framework for Capture Point Tracking
  7. Prediction of Future Trajectory
  8. Problem Setup for MPC Optimization
  9. Variable Weighting Parameter for CAM control
  10. Stepping Controller based on CP--MPC
  11. Overview of the Stepping Controller
  12. Parameter Decision based on CP--MPC
  13. Formulating a QP problem for Stepping Control
  14. Characteristic of Our Parameter Selection Approach compared to Previous Methods
  15. HQP--based Whole-Body Inverse Kinematics
  16. ...and 23 more sections

Figures (13)

  • Figure 1: Overall walking control framework: Three balance strategies for CP tracking control via CP--MPC, footstep position and step time determination by the stepping controller, and implementation through HQP--WBIK.
  • Figure 2: Decision process of variable weighting parameter.
  • Figure 3: Schematic representation of the stepping control; In order to control the CP offset, footstep position and step time are adjusted based on CP end-of-step dynamics.
  • Figure 4: Simulation results are presented for the robot's response to external forces applied along both the negative x- and y-directions, shown in (a) and (b) respectively.
  • Figure 5: Simulation results are presented for the robot's forward walking while overcoming four unexpected objects.
  • ...and 8 more figures