DcnnGrasp: Towards Accurate Grasp Pattern Recognition with Adaptive Regularizer Learning

by   Xiaoqin Zhang, et al.
Memorial University of Newfoundland

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 global accuracy for the case of testing a novel object on the RGB-D Object dataset.


page 1

page 3

page 9

page 10


Grasp-type Recognition Leveraging Object Affordance

A key challenge in robot teaching is grasp-type recognition with a singl...

Logical-Combinatorial Approaches in Dynamic Recognition Problems

A pattern recognition scenario, where instead of object classification i...

Building pattern recognition applications with the SPARE library

This paper presents the SPARE C++ library, an open source software tool ...

Application of Hopfield Network to Saccades

Human eye movement mechanisms (saccades) are very useful for scene analy...

Pattern Recognition for Conditionally Independent Data

In this work we consider the task of relaxing the i.i.d assumption in pa...

Efficient training and design of photonic neural network through neuroevolution

Recently, optical neural networks (ONNs) integrated in photonic chips ha...

Evaluating Classifier Confidence for Surface EMG Pattern Recognition

Surface electromyogram (EMG) can be employed as an interface signal for ...

Please sign up or login with your details

Forgot password? Click here to reset