TY - GEN
T1 - Reproducing arm movements based on Pose Estimation with robot programming by demonstration
AU - Fernandez-Ramos, Oscar
AU - Johnson-Yanez, Diego
AU - Ugarte, Willy
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Teaching robot movements has always been considered a complex topic in which there is much interest, since the slightest change in the robot's programming can generate a high downtime that can last for months. In this work, we carried out a study of human movements to implement a new method of Robot Programming by Demonstration (RPbD) using neural networks. Current methods require specialists with high mathematical and logical knowledge to teach robot movements. Using a famous pose estimation algorithm called OpenPose and a 3D lifting method we obtain the estimated pose of the person arm in a simnlated 3D space. Then, we use various classification tools to translate it to the robot. The results show that it is feasible to make robot programming more accessible using pose estimation.
AB - Teaching robot movements has always been considered a complex topic in which there is much interest, since the slightest change in the robot's programming can generate a high downtime that can last for months. In this work, we carried out a study of human movements to implement a new method of Robot Programming by Demonstration (RPbD) using neural networks. Current methods require specialists with high mathematical and logical knowledge to teach robot movements. Using a famous pose estimation algorithm called OpenPose and a 3D lifting method we obtain the estimated pose of the person arm in a simnlated 3D space. Then, we use various classification tools to translate it to the robot. The results show that it is feasible to make robot programming more accessible using pose estimation.
KW - Neural Networks
KW - OpenPose
KW - Pose Estimation
KW - Robot Programming by Demonstration
UR - https://www.scopus.com/pages/publications/85123947866
U2 - 10.1109/ICTAI52525.2021.00049
DO - 10.1109/ICTAI52525.2021.00049
M3 - Contribución a la conferencia
AN - SCOPUS:85123947866
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 294
EP - 298
BT - Proceedings - 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence, ICTAI 2021
PB - IEEE Computer Society
T2 - 33rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2021
Y2 - 1 November 2021 through 3 November 2021
ER -