Decision-theoretic layered robotic control architecture

One of the current methods for developing task control software for robots is a layering approach. This approach generally consists of a symbolic planner, a task sequencer, and a behavioral robotic controller. The task sequencer is responsible for taking a command from an abstract plan and selecting which robot level actions and behaviors to execute. This representation leads to a robust functioning software control for a robot and a single task [Bonnasso and Kortenkamp, 1996]. When the robot must be reconfigured for a new task, elements must be added to the sequencer, and behaviors to the behavioral controller. We are currently developing a decision-theoretic planner to function as the planning and sequencing layers for the architecture. It is our expectation that using a decision-theoretic planner as the sequencer will reduce th