One of the tasks that continues to prove difficult in robotics is the ability to grasp objects of varying shapes. It is time-consuming to acquire large amounts of real-world data in order to train accurate classifiers that can predict the success or failure of a grasp. To solve this issue, we examine using artificially generated surrogate, or substitute, datasets as replacement training data for more expensive physically-tested training data. By dividing up the grasp space using kd-trees, we demonstrate that surrogate datasets can be efficiently leveraged to produce high-precision data in specific areas of the grasp space. This greatly eases the burden of collecting data by only requiring physical testing in areas where surrogate datasets have little expertise.Key Words: robotic grasping, kd-tree, Gaussian Process, Logistic Regression, classificatio
