We have developed a decision-theoretic planner based upon the Graphplan planning algorithm, DT-Graphplan. DT-Graphplan reasons about probabilities, costs, and rewards at a propositional level, reconstructing limited state information. We are applying the planner to our robot task architecture to function on a miniature golf domain. By incorporating decision theory into planning, we seek to reduce the representational gap between behavior-based robotic controllers and constraint-based symbolic planners. Introduction This paper discusses DT-Graphplan, a decision-theoretic planner that we use as the planning and sequencing layers for a layered robotic architecture. By using a planner at both the symbolic planner level and the sequencer level we hope for a reduction of the work needed to reconfigure a robot for a new task. We will verify this by creating one set of behavior controllers for our robots and demonstrating the effectiveness of the controllers on multiple diverse plans..