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This paper proposes a novel orientation-aware model predictive control (MPC)for dynamic humanoid walking that can plan footstep locations online. Insteadof a point-mass model, this work uses the augmented single rigid body model(aSRBM) to enable the MPC to leverage orientation dynamics and steppingstrategy within a unified optimization framework. With the footstep location aspart of the decision variables in the aSRBM, the MPC can reason about steppingwithin the kinematic constraints. A task-space controller (TSC) tracks the bodypose and swing leg references output from the MPC, while exploiting thefull-order dynamics of the humanoid. The proposed control framework is suitablefor real-time applications since both MPC and TSC are formulated as quadraticprograms. Simulation investigations show that the orientation-aware MPC-basedframework is more robust against external torque disturbance compared tostate-of-the-art controllers using the point mass model, especially when thetorso undergoes large angular excursion. The same control framework can alsoenable the MIT Humanoid to overcome uneven terrains, such as traversing a wavefield.

Orientation-Aware Model Predictive Control with Footstep Adaptation for Dynamic Humanoid Walking