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

Maxime Devanne 1 Sao Mai Nguyen 2
1 IMT Atlantique - IMT Atlantique Bretagne-Pays de la Loire
2 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 robots' understanding to patient's physical limitations during the assessment of rehabilitation exercises. Experimental evaluation demonstrates promising results for both robot imitation and model adaptation according to the patients' limitations.
Keywords : motion analysis shared Gaussian Process Latent Variable Model Physical rehabilitation Robot imitation transfer knowledge
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Maxime Devanne, Sao Mai Nguyen. Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand their Physical Limitations. Leal-Taixé L; Roth S. Lecture Notes in Computer Science, 11130, ⟨Springer, Cham⟩, pp.190-197, 2019, Computer Vision – ECCV 2018 Workshops, ⟨10.1007/978-3-030-11012-3_15⟩. ⟨hal-01891414v3⟩

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