Every Action Based Sensor
This work addresses a foundational limitation in robot planning theory, offering a more comprehensive framework for minimal information requirements, though it is incremental relative to the established theory.
The authors identified a gap in Erdmann's classical theory of action-based sensors for robot planning, showing that existing methods using backchained plans miss some sensors and fail for certain guaranteed plans. They generalized the approach to produce sets of sensors, achieving a complete characterization of action-based sensors for planning problems.
In studying robots and planning problems, a basic question is what is the minimal information a robot must obtain to guarantee task completion. Erdmann's theory of action-based sensors is a classical approach to characterizing fundamental information requirements. That approach uses a plan to derive a type of virtual sensor which prescribes actions that make progress toward a goal. We show that the established theory is incomplete: the previous method for obtaining such sensors, using backchained plans, overlooks some sensors. Furthermore, there are plans, that are guaranteed to achieve goals, where the existing methods are unable to provide any action-based sensor. We identify the underlying feature common to all such plans. Then, we show how to produce action-based sensors even for plans where the existing treatment is inadequate, although for these cases they have no single canonical sensor. Consequently, the approach is generalized to produce sets of sensors. Finally, we show also that this is a complete characterization of action-based sensors for planning problems and discuss how an action-based sensor translates into the traditional conception of a sensor.