Robotic object manipulation with multilevel part-based model in RGB-D data

The performance of robotic object manipulation relies heavily on the selection of object model. In this article, we develop a multilevel part-based object model by applying latent support vector machine to training a hierarchical object structure. We implement our method with a robot arm and a depth sensor in Robot Operating System, and then we compare the recognition performance of this model with established methods on a point cloud data set and show the manipulation performance of our model on three practical tasks. The result demonstrates that our robot recognizes and manipulates objects more accurately with this multilevel part-based object model.