Robotic rehabilitation has demonstrated slight positive effects compared to traditional care, but there is still a lack of targeted high-level control strategies in the current state-of-the-art for minimizing pathological motor behaviors. In this study, we analyzed upper-limb motion capture data from healthy subjects performing a pick-and-place task to identify task-specific variability in postural patterns. The results revealed consistent behaviors among subjects, presenting an opportunity to develop a novel extraction method for variable volume references based solely on observations from healthy individuals. These human-centered references were tested on a simulated 4 degrees-of-freedom upper-limb exoskeleton, showing its compliant adaptation to the path considering the variance in healthy subjects' motor behavior.