Multiple task optimization with a mixture of controllers for motion generation

Simultaneous mastering of multiple tasks during motion generation is challenging. Traditional null-space based approaches for redundant robots implement a strict, hierarchical prioritization for tracking multiple objectives. In consequence, these schemes are not suited to impose smooth priorities or changing them during motion execution. A recently developed mixture of controller approach superpose torques from several control modules for motion generation and thereby enables to flexibly impose priorities for pursuing different goals in parallel. The main contribution of this paper is the development of a framework which allows for automatic derivation of suitable mixture coefficients which represent priorities. The functionality of the optimization framework is demonstrated for a virtual 3 DOF pendulum and the humanoid robot COMAN.