Fast Object Recognition for Humanoid Robots by Using Deep Learning Models with Small Structure

Abstract

In these days, the humanoid robots are expected to help people in healthcare, house and hotels, industry, military and the other security environments by performing specific tasks or to replace with people in dangerous scenarios. For this purpose, the humanoid robots should be able to recognize objects and then to do the desired tasks. In this study, it is aimed for Robotis-Op3 humanoid robot to recognize the different shaped objects with deep learning methods. First of all, new models with small structure of Convolutional Neural Networks (CNNs) were proposed. Then, the popular deep neural networks models such as VGG16 and Residual Network (ResNet) that is good at object recognition were used for comparing at recognizing the objects. The results were compared in terms of training time, performance, and model complexity. Simulation results show that new models with small layer structure produced higher performance than complex models.