The main goal of the paper is to provide Pepper with a near real-time object recognition system based on deep neural networks. The proposed system is based on YOLO (You Only Look Once), a deep neural network that is able to detect and recognize objects robustly and at a high speed. In addition, considering that YOLO cannot be run in the Pepper's internal computer in near real-time, we propose to use a Backpack for Pepper, which holds a Jetson TK1 card and a battery. By using this card, Pepper is able to robustly detect and recognize objects in images of 320x320 pixels at about 5 frames per second.
@article{arxiv.1811.08352,
title = {Near Real-Time Object Recognition for Pepper based on Deep Neural Networks Running on a Backpack},
author = {Esteban Reyes and Cristopher Gómez and Esteban Norambuena and Javier Ruiz-del-Solar},
journal= {arXiv preprint arXiv:1811.08352},
year = {2018}
}
Comments
Proceedings of 22th RoboCup International Symposium, Montreal, Canada, 2018