🤖 AI Summary
This work addresses the challenge of achieving human-like robust balance control for bipedal robots in real-world environments. Methodologically, it proposes a unified framework integrating ankle, hip, and stepping strategies: (i) a variable-weight angular momentum damping controller is designed based on capture point (CP) dynamics; and (ii) a hierarchical architecture coupling model predictive control (MPC) with a gait controller enables online gait timing optimization and high-precision CP tracking. The key contribution is the first integration of angular momentum damping with a variable-weight mechanism into an MPC framework, enabling real-time, multi-strategy cooperative balance regulation. Simulation and hardware experiments demonstrate significantly improved disturbance rejection and superior balance performance compared to state-of-the-art quadratic-programming-based CP controllers.
📝 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.
