Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand Their Physical Limitations
Conference paper
First Online:
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.
Keywords
Robot imitation Transfer knowledge Physical rehabilitation Shared Gaussian Process Latent Variable Model Motion analysisNotes
Acknowledgement
The research work presented in this paper is partially supported by the EU FP7 grant ECHORD++ KERAAL, by the the European Regional Fund (FEDER) via the VITAAL Contrat Plan Etat Region and by project AMUSAAL funded by Region Brittany, France.
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