  We present a computationally efficient method for online planning of bipedal
walking trajectories with push recovery. In particular, the proposed
methodology fits control architectures where the Divergent-Component-of-Motion
(DCM) is planned beforehand, and adds a step adapter to adjust the planned
trajectories and achieve push recovery. Assuming that the robot is in a single
support state, the step adapter generates new positions and timings for the
next step. The step adapter is active in single support phases only, but the
proposed torque-control architecture considers double support phases too. The
key idea for the design of the step adapter is to impose both initial and final
DCM step values using an exponential interpolation of the time varying ZMP
trajectory.This allows us to cast the push recovery problem as a Quadratic
Programming (QP) one, and to solve it online with state-of-the-art optimisers.
The overall approach is validated with simulations of the torque-controlled 33
kg humanoid robot iCub. Results show that the proposed strategy prevents the
humanoid robot from falling while walking at 0.28 m/s and pushed with external
forces up to 150 Newton for 0.05 seconds.
