  To enable robots to achieve high level objectives, engineers typically write
scripts that apply existing specialized skills, such as navigation, object
detection and manipulation to achieve these goals. Writing good scripts is
challenging since they must intelligently balance the inherent stochasticity of
a physical robot's actions and sensors, and the limited information it has. In
principle, AI planning can be used to address this challenge and generate good
behavior policies automatically. But this requires passing three hurdles.
First, the AI must understand each skill's impact on the world. Second, we must
bridge the gap between the more abstract level at which we understand what a
skill does and the low-level state variables used within its code. Third, much
integration effort is required to tie together all components. We describe an
approach for integrating robot skills into a working autonomous robot
controller that schedules its skills to achieve a specified task and carries
four key advantages. 1) Our Generative Skill Documentation Language (GSDL)
makes code documentation simpler, compact, and more expressive using ideas from
probabilistic programming languages. 2) An expressive abstraction mapping (AM)
bridges the gap between low-level robot code and the abstract AI planning
model. 3) Any properly documented skill can be used by the controller without
any additional programming effort, providing a Plug'n Play experience. 4) A
POMDP solver schedules skill execution while properly balancing partial
observability, stochastic behavior, and noisy sensing.
