  We propose to quantify the effect of sensor and actuator uncertainties on the
control of the center of mass and center of pressure in legged robots, since
this is central for maintaining their balance with a limited support polygon.
Our approach is based on robust control theory, considering uncertainties that
can take any value between specified bounds. This provides a principled
approach to deciding optimal feedback gains. Surprisingly, our main observation
is that the sampling period can be as long as 200 ms with literally no impact
on maximum tracking error and, as a result, on the guarantee that balance can
be maintained safely. Our findings are validated in simulations and experiments
with the torque-controlled humanoid robot Toro developed at DLR. The proposed
mathematical derivations and results apply nevertheless equally to biped and
quadruped robots.
