As robotic agents become increasingly present in human environments, task completion rates during human-robot interaction is an important topic of research. Safe collaborative robots executing tasks under human supervision often augment their perception and planning capabilities through traded or shared control schemes. In this paper, we present a quantitatively defined model for sliding-scale autonomy, in which levels of autonomy are determined by the relative efficacy of autonomous subroutines. We experimentally test the resulting Variable Autonomy Planning (VAP) algorithm against a traditional traded control scheme in a pick-and-place task. Results show that prioritizing autonomy levels with higher success rates as encoded into VAP, allows users to effectively and intuitively select optimal autonomy levels for efficient task completion.