DcnnGrasp: Towards Accurate Grasp Pattern Recognition with Adaptive Regularizer Learning

• URL: http://arxiv.org/abs/2205.05218v1
• Date: Wed, 11 May 2022 00:34:27 GMT
• Title: DcnnGrasp: Towards Accurate Grasp Pattern Recognition with Adaptive Regularizer Learning
• Authors: Xiaoqin Zhang, Ziwei Huang, Jingjing Zheng, Shuo Wang, and Xianta Jiang
• Abstract summary: 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.
• Score: 13.08779945306727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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