Disturbance compensation and step optimization for push recovery

To operate in human environments, robots must be able to withstand external disturbances. Small disturbances can be stabilized through momentum regulation, but larger ones require steps to prevent falling. This work presents two new techniques for disturbance rejection. The first is an extension of divergent component of motion (DCM) and capture point tracking controllers that augments a PI feedback control law with a disturbance observer. This is used to estimate transient disturbances through momentum-rate-of-change error. For larger disturbances, we present a novel optimization-based framework based on the DCM dynamics that uses a quadratic program to compute the desired ground reaction forces and recovery step location. Using optimization gives a flexibility that enables planning angular-momentum-rate-of-change trajectories to help reduce recovery step length. We then illustrate the effectiveness of these methods with hardware and simulation experiments of the THOR humanoid.