While mobile robots reliably perform each service task by accurately localizing and safely navigating avoiding obstacles, they do not respond in any other way to their surroundings. We can make the robots more responsive to their environment by equipping them with models of multiple tasks and a way to interrupt a specific task and switch to another task based on observations. However the challenges of a multiple task model approach include selecting a task model to execute based on observations and having a potentially large set of observations associated with the set of all individual task models. We present a novel two-step solution. First, our approach leverages the tasks' policies and an abstract representation of their states, and learns which task should be executed at each given world state. Secondly, the algorithm uses the learned tasks and identifies the observation stimuli that trigger the interruption of one task and the switch to another task. We show that our solution using the switching stimuli compares favorably to the naive approach of learning a combined model for all the tasks. Moreover, leveraging the stimuli significantly decreases the amount of sensory input processing during the execution of tasks.