  This paper proposes a novel orientation-aware model predictive control (MPC)
for dynamic humanoid walking that can plan footstep locations online. Instead
of a point-mass model, this work uses the augmented single rigid body model
(aSRBM) to enable the MPC to leverage orientation dynamics and stepping
strategy within a unified optimization framework. With the footstep location as
part of the decision variables in the aSRBM, the MPC can reason about stepping
within the kinematic constraints. A task-space controller (TSC) tracks the body
pose and swing leg references output from the MPC, while exploiting the
full-order dynamics of the humanoid. The proposed control framework is suitable
for real-time applications since both MPC and TSC are formulated as quadratic
programs. Simulation investigations show that the orientation-aware MPC-based
framework is more robust against external torque disturbance compared to
state-of-the-art controllers using the point mass model, especially when the
torso undergoes large angular excursion. The same control framework can also
enable the MIT Humanoid to overcome uneven terrains, such as traversing a wave
field.
