The overall performance of a robotic system is commonly expressed by a single scenario-specific metric which is supposed to be optimized. However, the metric describing the performance of a single subtask within a scenario may be different. Nevertheless, the scenario performance is most likely dependent on the subtask performances but a mutual transformation is not straightforward in general, especially in complex robotic systems. This leads to what we call the common pricing problem, i.e. the problem to determine the functional relationship among a set of different performance criteria and then account for this relationship in the various optimizations throughout all system layers. In this paper we present an approach to first learn a probabilistic model of the metric interdependencies, and thereafter utilize this model for performance estimation and optimal task parameterization during planning and execution respectively. The proposed method is validated in a simulation.