Record Details
Predicting Robotic Grasps Using Surrogate Datasets
ScholarsArchive at Oregon State University
| Field | Value |
|---|---|
| Title | Predicting Robotic Grasps Using Surrogate Datasets |
| Names |
Unrath, Matthew Paul Nishita
(creator) Wong, Weng-Keen (advisor) |
| Date Issued | 2014-06-02 (iso8601) |
| Note | Honors Bachelor of Science (HBS) |
| Abstract | 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. |
| Genre | Thesis |
| Topic | robotic grasping |
| Identifier | http://hdl.handle.net/1957/52433 |
