  As robots are deployed in complex situations, engineers and end users must
develop a holistic understanding of their capabilities and behaviors. Existing
research focuses mainly on factors related to task completion, such as success
rate, completion time, or total energy consumption. Other factors like
collision avoidance behavior, trajectory smoothness, and motion legibility are
equally or more important for safe and trustworthy deployment. While methods
exist to analyze these quality factors for individual trajectories or
distributions of trajectories, these statistics may be insufficient to develop
a mental model of the controller's behaviors, especially uncommon behaviors. We
present RoCUS: a Bayesian sampling-based method to find situations that lead to
trajectories which exhibit certain behaviors. By analyzing these situations and
trajectories, we can gain important insights into the controller that are
easily missed in standard task-completion evaluations. On a 2D navigation
problem and a 7 degree-of-freedom (DoF) arm reaching problem, we analyze three
controllers: a rapidly exploring random tree (RRT) planner, a dynamical system
(DS) formulation, and a deep imitation learning (IL) or reinforcement learning
(RL) model. We show how RoCUS can uncover insights to further our understanding
about them beyond task-completion aspects. The code is available at
https://github.com/YilunZhou/RoCUS.
