Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand their Physical Limitations

Maxime Devanne 1, 2 Sao Mai Nguyen 1, 2
1 Lab-STICC_IMTA_CID_IHSEV
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
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.
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Communication dans un congrès
Workshop on Transferring and Adapting Source Knowledge in Computer Vision, in conjunction with ECCV2018, Sep 2018, Munich, Germany
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https://hal.archives-ouvertes.fr/hal-01886728
Contributeur : Maxime Devanne <>
Soumis le : mercredi 3 octobre 2018 - 10:45:31
Dernière modification le : jeudi 18 octobre 2018 - 08:06:01

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Task_CV_ECCV2018.pdf
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  • HAL Id : hal-01886728, version 1

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Maxime Devanne, Sao Mai Nguyen. Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand their Physical Limitations. Workshop on Transferring and Adapting Source Knowledge in Computer Vision, in conjunction with ECCV2018, Sep 2018, Munich, Germany. 〈hal-01886728v1〉

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