  For collaborative robots to become useful, end users who are not robotics
experts must be able to instruct them to perform a variety of tasks. With this
goal in mind, we developed a system for end-user creation of robust task plans
with a broad range of capabilities. CoSTAR: the Collaborative System for Task
Automation and Recognition is our winning entry in the 2016 KUKA Innovation
Award competition at the Hannover Messe trade show, which this year focused on
Flexible Manufacturing. CoSTAR is unique in how it creates natural abstractions
that use perception to represent the world in a way users can both understand
and utilize to author capable and robust task plans. Our Behavior Tree-based
task editor integrates high-level information from known object segmentation
and pose estimation with spatial reasoning and robot actions to create robust
task plans. We describe the cross-platform design and implementation of this
system on multiple industrial robots and evaluate its suitability for a wide
variety of use cases.
