Facial Recognition and Pathfinding on the Humanoid Robot Pepper as a Starting Point for Social Interaction

Dannecker, Achim; Hertig, Daniel

doi:10.1007/978-3-030-48332-6_10

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Nowadays, the humanoid robots are increasingly used in a lot of areas, such as the medicine, the education, the healthcare, the logistics and the house and hotel services, to improve the quality of human life (Andtfolk et al, 2021;Angelopoulos et al, 2021;Dannecker & Hertig, 2021;Garcia-Haro et al, 2021;Nenchev et al, 2018;Oliver et al, 2021). The humanoid robots were used instead of humans or together with humans (Ambrose et al, 2001;Chohra & Madani, 2018;Fitzpatrick & Metta, 2003;Levine et al, 2016;Reforgiato Recupero, 2021;Sakagami et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

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

Aslan

Uçar

Güzeliş

2021

Journal of Information and Telecommunication

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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 , 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.

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