The planner integrates two algorithms one based on offline policy computation and one based on online search. It chooses one of them, depending on the state space size of the given task. For small instances, the offline algorithm, SARSOP [1], is executed. SARSOP is an efficient point-based algorithm that backs up from belief points which are close to -Y´optimally reachable¡ from the given initial belief. For large instances, the online algorithm, derived from POMCP [2], is triggered. It uses particle filters to represent beliefs and UCT, a Monte-Carlo simulation method, to evaluate the value of potential actions. [1] H. Kurniawati, D. Hsu, and W.S. Lee. SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces. In Proc. Robotics: Science and Systems, 2008. [2] D. Silver, and J. Veness. Monte-carlo planning in large POMDPs. In NIPS, 2010.