  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.
