  The task of grasp pattern recognition aims to derive the applicable grasp
types of an object according to the visual information. Current
state-of-the-art methods ignore category information of objects which is
crucial for grasp pattern recognition. This paper presents a novel dual-branch
convolutional neural network (DcnnGrasp) to achieve joint learning of object
category classification and grasp pattern recognition. DcnnGrasp takes object
category classification as an auxiliary task to improve the effectiveness of
grasp pattern recognition. Meanwhile, a new loss function called joint
cross-entropy with an adaptive regularizer is derived through maximizing a
posterior, which significantly improves the model performance. Besides, based
on the new loss function, a training strategy is proposed to maximize the
collaborative learning of the two tasks. The experiment was performed on five
household objects datasets including the RGB-D Object dataset, Hit-GPRec
dataset, Amsterdam library of object images (ALOI), Columbia University Image
Library (COIL-100), and MeganePro dataset 1. The experimental results
demonstrated that the proposed method can achieve competitive performance on
grasp pattern recognition with several state-of-the-art methods. Specifically,
our method even outperformed the second-best one by nearly 15% in terms of
global accuracy for the case of testing a novel object on the RGB-D Object
dataset.
