Akademik Veri Yönetim Sistemi
33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Türkiye, 25 - 28 Haziran 2025, (Tam Metin Bildiri)
Today, with the rapid spread of robots, the significance of robot training has increased. Imitation learning plays a significant role in robot training, and numerous scientific studies are published in this field each year. In this method, robots can be controlled by imitating human movements and this process is called teleoperation. In teleoperation studies, the mapping between human and robot skeletal systems is performed using two fundamental approaches: the mathematical model and the artificial intelligence model. However, the number of datasets in the literature that simultaneously record human and robot skeletal data to enable a comprehensive comparison of these two approaches is quite limited. In this study, we created two separate datasets containing human and robot skeletal data and experimentally compared two different teleoperation methods. The experimental findings indicate that the artificial intelligence model, when trained with unbiased datasets, outperforms the mathematical model in teleoperation processes.