This paper proposes a novel control framework for agile and robust bipedal locomotion, addressing model discrepancies between full-body and reduced-order models. Specifically, assumptions such as constant centroidal inertia have introduced significant challenges and limitations in locomotion tasks. To enhance the agility and versatility of full-body humanoid robots, we formalize a Model Predictive Control (MPC) problem that accounts for the variable centroidal inertia of humanoid robots within a convex optimization framework, ensuring computational efficiency for real-time operations. In this formulation, we incorporate a centroidal inertia network designed to predict the variable centroidal inertia over the MPC horizon, taking into account the swing foot trajectories-an aspect often overlooked in ROM-based MPC frameworks. Moreover, we enhance the performance and stability of locomotion behaviors by synergizing the MPC-based approach with whole-body control (WBC). The effectiveness of our proposed framework is validated through simulations using our full-body humanoid robot, DRACO 3, demonstrating dynamic behaviors.

翻译：本文提出了一种新颖的控制框架，用于实现敏捷且鲁棒的双足运动，旨在解决全身模型与降阶模型之间的模型差异问题。具体而言，诸如恒定质心动量矩等假设在运动任务中引入了显著的挑战与限制。为提升全身仿人机器人的敏捷性与多功能性，我们在一个凸优化框架内形式化了一个模型预测控制问题，该问题考虑了仿人机器人的可变质心动量矩，从而确保了实时操作的计算效率。在此公式中，我们引入了一个质心动量矩网络，该网络旨在预测MPC预测时域内的可变质心动量矩，同时考虑了摆动足轨迹——这一因素在基于降阶模型的MPC框架中常被忽视。此外，我们通过将基于MPC的方法与全身控制协同整合，提升了运动行为的性能与稳定性。我们提出的框架的有效性通过使用我们的全身仿人机器人DRACO 3进行的仿真实验得到验证，展示了动态行为。