Publication

Attention deep learning based model for predicting the 3D human body pose using the robot human handover phases

Conference Article

Conference

IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

Edition

2021

Pages

161-166

Doc link

https://doi.org/10.1109/RO-MAN50785.2021.9515402

File

Download the digital copy of the doc pdf document

Abstract

This work proposes a human motion prediction model for handover operations. We use in this work, the different phases of the handover operation to improve the human motion predictions. Our attention deep learning based model takes into account the position of the robot’s End Effector and the phase in the handover operation to predict future human poses. Our model outputs a distribution of possible positions rather than one deterministic position, a key feature in order to allow robots to collaborate with humans. The attention deep learning based model has been trained and evaluated with a dataset created using human volunteers and an anthropomorphic robot, simulating handover operations where the robot is the giver and the human the receiver. For each operation, the human skeleton is obtained with an Intel RealSense D435i camera attached inside the robot’s head. The results shown a great improvement of the human’s right hand prediction and 3D body compared with other methods.

Categories

automation.

Author keywords

attention deep learning, human motion prediction, handover operation

Scientific reference

J. Laplaza, A. Pumarola, F. Moreno-Noguer and A. Sanfeliu. Attention deep learning based model for predicting the 3D human body pose using the robot human handover phases, 2021 IEEE International Symposium on Robot and Human Interactive Communication, 2021, Vancouver (Canada), pp. 161-166.