Humans and robots team together to perform tasks in various domains. Some tasks are easier to perform than others, but little work focuses on discovering the underlying mechanisms that affect perceived difficulty and task performance. To fill this gap, we propose a formalized approach to task characterization for human-robot teams using Taguchi design of experiments and conjoint analysis. With this, we conduct a 20 person study where participants operate a 6 degree of freedom robotic arm to perform manipulations defined by 6 kinematic features. We find that rotational features of a task contribute significantly more to decreased performance and increased difficulty than translational features. The participants also perform the activities with autonomy assistance. The data shows a reduction in the effect of these features on performance and difficulty when assistance is active. Furthermore, we examine when to trigger assistance based on thresholds set from outlier detection. The analysis indicates that rotational features and features leading to kinematic singularities are useful for triggering assistance.