Underactuated adaptive hands simplify grasping tasks but it is difficult to model their interactions with objects during in-hand manipulation. Learned data-driven models have been recently shown to be efficient in motion planning and control of such hands. Still, the accuracy of the models is limited even with the addition of more data. This becomes important for long horizon predictions, where errors are accumulated along the length of a path. Instead of throwing more data into learning the transition model, this work proposes to rather invest a portion of the training data in a critic model. The critic is trained to estimate the error of the transition model given a state and a sequence of future actions, along with information of past actions. The critic is used to reformulate the cost function of an asymptotically optimal motion planner. Given the critic, the planner directs planned paths to less erroneous regions in the state space. The approach is evaluated against standard motion planning on simulated and real hands. The results show that it outperforms an alternative where all the available data is used for training the transition model without a critic.