Publication
Context and intention aware 3D human body motion prediction using an attention deep learning model in handover tasks
Conference Article
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Edition
2022
Pages
4743-4748
Doc link
https://doi.org/10.1109/IROS47612.2022.9981465
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Authors
Projects associated
Abstract
This work explores how contextual information and human intention affect the motion prediction of humans during a handover operation with a social robot. By classifying human intention in four different classes, we developed a model able to generate a different motion for each intention class. Furthermore, the model uses a multi-headed attention architecture to add contextual information to the pipeline, such as the position of the robot end effector (REE) or the position of obstacles in the interaction scene. We generate predictions up to two and half seconds in the future given an input sequence of one second containing the previous motion of the human. The results show an improvement of the prediction accuracy, both for the full skeleton prediction and the human hand used for the delivery. The model also allows to generate different sequences with the desired human intention.
Categories
artificial intelligence, computer vision, pattern recognition, pose estimation.
Author keywords
human motion prediction
Scientific reference
J. Laplaza, F. Moreno-Noguer and A. Sanfeliu. Context and intention aware 3D human body motion prediction using an attention deep learning model in handover tasks, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022, Kyoto, pp. 4743-4748.
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