Predicting Robotic Grasps Using Surrogate Datasets Public

http://ir.library.oregonstate.edu/concern/honors_college_theses/9880vs81z

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  • 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.
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