New convolutional neural network models for efficient object recognition with humanoid robots

Abstract

Humanoid robots are expected to manipulate the objects they have not previously seen in real-life environments. Hence it is important that the robots have the object recognition capability. However object recognition is still a challenging problem at different locations and different object positions in real time. The current paper presents four novel models with small structure based on Convolutional Neural Networks (CNNs) for object recognition with humanoid robots. In the proposed models a few combinations of convolutions are used to recognize the class labels. The MNIST and CIFAR-10 benchmark datasets are first tested on our models. The performance of the proposed models is shown by comparisons to that of the best state-of-the-art models. The models are then applied on the Robotis-Op3 humanoid robot to recognize the objects of different shapes. The results of the models are compared to those of the models such as VGG-16 and Residual Network-20 (ResNet-20) in terms of training and validation accuracy and loss parameter number and training time. The experimental results show that the proposed model exhibits high accurate recognition by the lower parameter number and smaller training time than complex models. Consequently the proposed models can be considered promising powerful models for object recognition with humanoid robots.