Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand Their Physical Limitations
Abstract
Assistive robotics and particularly robot coaches may be very helpful for rehabilitation healthcare. In this context, we propose a method based on Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a physiotherapist, a robot coach and a patient. Our model is able to map visual human body features to robot data in order to facilitate the robot learning and imitation. In addition, we propose to extend the model to adapt the robots’ understanding to patients’ physical limitations during assessment of rehabilitation exercises. Experimental evaluation demonstrates promising results for both robot imitation and model adaptation according to patients’ limitations.
Cite
Text
Devanne and Nguyen. "Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand Their Physical Limitations." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11012-3_15Markdown
[Devanne and Nguyen. "Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand Their Physical Limitations." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/devanne2018eccvw-generating/) doi:10.1007/978-3-030-11012-3_15BibTeX
@inproceedings{devanne2018eccvw-generating,
title = {{Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand Their Physical Limitations}},
author = {Devanne, Maxime and Nguyen, Sao Mai},
booktitle = {European Conference on Computer Vision Workshops},
year = {2018},
pages = {190-197},
doi = {10.1007/978-3-030-11012-3_15},
url = {https://mlanthology.org/eccvw/2018/devanne2018eccvw-generating/}
}