In this work, we provide a solution for posturing the anthropomorphic Robonaut-2 hand and arm for grasping based on visual information. A mapping between visual features extracted from a convolutional neural network (CNN) to grasp points is learned. We demonstrate that a pre-trained CNN for image classification can be applied to a grasping task based on a small set of grasping examples. Our approach takes advantage of the hierarchical nature of the CNN and identifies the 3D positions of features that capture the hierarchical support relations between filters in different CNN layers by tracing the activation of higher level features in the CNN backward. When this backward trace terminates in the RGB-D image, important manipulable structures comprising the objects are, thus, localized. These features located in different layers of the CNN are then associated to controllers belonging to different hierarchies of the robot morphology for grasping. A Grasping Dataset is collected using demonstrated hand object relationships for Robonaut-2 to evaluate the proposed approach in terms of the precision of the resulting preshape postures. We demonstrate that this approach outperforms base-line approaches in cluttered scenarios on the Grasping Dataset and a point cloud based approach on a grasping task using Robonaut-2.
Our work is also inspired by @cite_11 where a class model is composed of smaller models of parts; e.g., wheels are parts of a bicycle. We view CNNs similarly; if a higher layer filter response represents a high level structure, the lower layer filter responses that contribute to this higher layer response can be seen as representing local parts of this structure and may provide useful information for manipulating an object.
@cite_11
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We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.