In machine learning for robotics, training data quality assumes a crucial role. Many methods use exploration algorithms to select the most informative data points for the model, often ignoring the impact of measurement noise on data. This paper introduces a method to enhance dataset quality for model learning, optimizing a combination of exploration and active sensing metrics. We introduce a novel Exploration Gramian metric based on a Gaussian Process predicted covariance matrix, optimized to explore the state space regions where the knowledge about the unknown model is maximum. These are integrated with an active sensing metric (Constructibility Gramian) to mitigate measurement noise effects. The effectiveness of this approach is demonstrated through simulations on a unicycle and a quadruped robot, confirming that combining active sensing and exploration significantly enhances performance in model learning.