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Assistive robotics and particularly robot coaches may be very helpful forrehabilitation healthcare. In this context, we propose a method based onGaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between aphysiotherapist, a robot coach and a patient. Our model is able to map visualhuman body features to robot data in order to facilitate the robot learning andimitation. In addition , we propose to extend the model to adapt robots'understanding to patient's physical limitations during the assessment ofrehabilitation exercises. Experimental evaluation demonstrates promisingresults for both robot imitation and model adaptation according to thepatients' limitations.

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