Task-planning algorithms for robots must quickly select actions with high reward prospects despite the huge variability of their domains, and accounting for the high cost of performing the wrong action in the “real-world”. In response we propose an action selection method based on reward-shaping, for planning in (PO)MDP’s, that adds an informed action-selection bias but depends almost exclusively on a clear specification of the goal. Combined with a derived rollout policy for MCTS planners, we show promising results in relatively large domains of interest to robotics.