  How can we enable novice users to create effective task plans for
collaborative robots? Must there be a tradeoff between generalizability and
ease of use? To answer these questions, we conducted a user study with the
CoSTAR system, which integrates perception and reasoning into a Behavior
Tree-based task plan editor. In our study, we ask novice users to perform
simple pick-and-place assembly tasks under varying perception and planning
capabilities. Our study shows that users found Behavior Trees to be an
effective way of specifying task plans. Furthermore, users were also able to
more quickly, effectively, and generally author task plans with the addition of
CoSTAR's planning, perception, and reasoning capabilities. Despite these
improvements, concepts associated with these capabilities were rated by users
as less usable, and our results suggest a direction for further refinement.
