Context Attention: Human Motion Prediction Using Context Information and Deep Learning Attention Models

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Context Attention: Human Motion Prediction Using Context Information and Deep Learning Attention Models

Laplaza, Javier / Moreno-Noguer, Francesc / Sanfeliu, Alberto

This work proposes a human motion prediction model for handover operations. The model uses a multi-headed attention architecture to process the human skeleton data together with contextual data from the operation. This contextual data consists on the position of the robot’s End Effector (REE). The model input is a sequence of 5 s skeleton position and it outputs the predicted 2.5 future seconds position. We provide results of the human upper body and the human right hand or Human End Effector (HEE).

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 using OpenPose with an Intel RealSense D435i camera set inside the robot’s head. The results show a great improvement of the human’s right hand prediction and 3D body compared with other methods.

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Document information

Title :

Context Attention: Human Motion Prediction Using Context Information and Deep Learning Attention Models

Additional title:

Lect. Notes in Networks, Syst.

Contributors:

Tardioli, Danilo (editor) / Matellán, Vicente (editor) / Heredia, Guillermo (editor) / Silva, Manuel F. (editor) / Marques, Lino (editor) / Laplaza, Javier (author) / Moreno-Noguer, Francesc (author) / Sanfeliu, Alberto (author)

Conference:

Iberian Robotics conference ; 2022 ; Zaragoza, Spain November 23, 2022 - November 25, 2022

Published in:

ROBOT2022: Fifth Iberian Robotics Conference ; Chapter : 9 ; 102-112

Lecture Notes in Networks and Systems ; 589

Publication date :

2022-11-19

Size :

11 pages

ISBN :

978-3-031-21065-5 , 978-3-031-21064-8

ISSN :