The deployment of robots in populated environments is recently gaining more interest because of increased maturity and capability of this technology. In this context, sophisticated planning techniques are required because there is a need of increasing the complexity of the tasks that the robot can accomplish. In particular, there is a large emphasis on service robots, i.e., robots that can satisfy several user needs. In this paper, we present a practical framework based on a decision-theoretic formalism for generation and execution of robust plans for service robots. The proposed framework has been implemented and succesfully tested on service robots interacting with non-expert users in public environments, facing many sources of uncertainty and failures in task execution.