Dataset skewness can impact the optimality of policies learned by GOMs by introducing biases towards certain trajectories or outcomes present in the dataset. If the dataset is skewed towards specific behaviors or states, GOMs may prioritize those trajectories over others during policy optimization, leading to suboptimal performance on unseen tasks. To address this issue, one approach is to employ data augmentation techniques that balance out the distribution of trajectories in the dataset. By augmenting data from underrepresented areas of the state space or less frequent behaviors, GOMs can learn more robust policies that generalize well across various scenarios without being biased towards specific trajectories from skewed datasets.