  Stable bipedal walking is a key prerequisite for humanoid robots to reach
their potential of being versatile helpers in our everyday environments.
Bipedal walking is, however, a complex motion that requires the coordination of
many degrees of freedom while it is also inherently unstable and sensitive to
disturbances. The balance of a walking biped has to be constantly maintained.
The most effective way of controlling balance are well timed and placed
recovery steps -- capture steps -- that absorb the expense momentum gained from
a push or a stumble. We present a bipedal gait generation framework that
utilizes step timing and foot placement techniques in order to recover the
balance of a biped even after strong disturbances. Our framework modifies the
next footstep location instantly when responding to a disturbance and generates
controllable omnidirectional walking using only very little sensing and
computational power. We exploit the open-loop stability of a central pattern
generated gait to fit a linear inverted pendulum model to the observed center
of mass trajectory. Then, we use the fitted model to predict suitable footstep
locations and timings in order to maintain balance while following a target
walking velocity. Our experiments show qualitative and statistical evidence of
one of the strongest push-recovery capabilities among humanoid robots to date.
