DBMP/S - Database-Driven Macro Planning for STRIPS


Planning in an on-line robotics context has the specific requirement of a short planning duration. A property of typical contemporary scenarios is that (mobile) robots perform similar or even repeating tasks during operation. With these robot domains in mind, we propose database-driven macro planning for STRIPS (DBMP/S) that learns macros — action sequences that frequently appear in plans — from experience for PDDL-based planners. Planning duration is improved over time by off-line processing of seed plans using a scalable database. The approach is indifferent about the specific planner by representing the resulting macros again as actions with preconditions and effects determined based on the actions contained in the macro. For some domains we have used separate planners for learning and execution exploiting their respective strengths. Initial results based on some IPC domains and a logistic robot scenario show signifi cantly improved (over non-macro planners) or slightly better and comparable (to existing macro planners) performance.


In the paper Initial Results on Generating Macro Actions from a Plan Database for Planning on Autonomous Mobile Robots we describe our approach to database-driven macro planning. We describe the general architecture of the system, explain how we collect plans and identify frequent action sequences from the database, and we describe how we generate macro actions from those action sequences.


DBMP/S Architecture

The source code of DBMP/S is available here.