  Quadratic Programs (QPs) have become a mature technology for the control of
robots of all kinds, including humanoid robots. One aspect has been largely
overlooked, however, which is the accuracy with which these QPs should be
solved. Typical QP solvers aim at providing solutions accurate up to floating
point precision ($\approx10^{-8}$). Considering physical quantities expressed
in SI or similar units (meters, radians, etc.), such precision seems completely
unrelated to both task requirements and hardware capacity. Typically, humanoid
robots never achieve, nor are capable of achieving sub-millimeter precision in
manipulation tasks. With this observation in mind, our objectives in this paper
are two-fold: first examine how the QP solution accuracy impacts the resulting
robot motion accuracy, then evaluate how a reduced solution accuracy
requirement can be leveraged to reduce the corresponding computational effort.
Numerical experiments with a dynamic simulation of a HRP-4 robot indicate that
computational effort can be divided by more than 20 while maintaining the
desired motion accuracy.
