We have been developing ROGUE, an architecture that integrates high-level planning with a low-level executing robotic agent. ROGUE is designed as the office gofer task planner for Xavier the robot. User requests are interpreted as high-level planning goals, such as getting coffee, and picking up and delivering mail or faxes. Users post tasks asynchronously and RoauE controls the corresponding planning and execution continuous process. This paper presents the extensions to a nonlinear state-space planning algorithm to allow for the interaction to the robot executor. We focus on presenting how executable steps are identified based on the planning model and the predicted execution per-formance; how interrupts from users requests are handled and incorporated into the system; how executable plans are merged according to their priorities; and how monitoring execution can add more perception knowledge to the planning and possible needed replannlng processes. The complete ROGUE system will learn from its planning and execution experiences to improve upon its own behaviour with time. We finalize the paper by briefly discussing ROGUE’s learning opportunities.